General AI research publications from MBZUAI, KAUST, TII, QCRI, and other GCC institutions, including conference papers, journal articles, and preprints.
This paper introduces a method for quantifying the transferability of architectural components in Single Image Super-Resolution (SISR) models, termed "Universality," and proposes a Universality Assessment Equation (UAE). Guided by the UAE, the authors design optimized modules, Cycle Residual Block (CRB) and Depth-Wise Cycle Residual Block (DCRB), and demonstrate their effectiveness across various datasets and low-level tasks. Results show that networks using these modules outperform state-of-the-art methods, achieving improved PSNR or parameter reduction.
The United Arab Emirates has reportedly made a significant advancement in its efforts to compete in the global large language model (LLM) development landscape. This initiative signifies a strategic move by the UAE to establish itself as a key player in frontier AI research and innovation. This 'bold leap' likely encompasses new investments, research programs, or the release of an advanced LLM. Why it matters: This development underscores the UAE's ambition to leverage AI for economic diversification and technological leadership in the Middle East and globally.
KAUST researchers have published a study in Nature Genetics detailing genomic analysis of wild rice relatives. The study examined nine tetraploid and two diploid wild relatives of rice, finding significant genetic diversity due to transposable elements. This diversity includes genes that confer resilience to heat, drought, and salinity. Why it matters: These findings can help improve rice yields, introduce rice cultivation to currently untenable regions, and protect rice crops against climate change, especially in the Middle East.
KAUST researchers have developed a detailed 3D dynamic model using data from the February 2023 Turkiye earthquake to improve earthquake simulations. The model incorporates 3D fault geometry and Earth structure for realistic simulations of ground shaking. It explains complex ground shaking patterns and the impact of supershear ruptures, which can amplify damage far from the epicenter. Why it matters: This research provides a more accurate understanding of earthquake rupture processes, crucial for seismic hazard assessment and infrastructure planning in seismically active regions like the Middle East.
KAUST researchers contributed to an international collaboration demonstrating that an ionic salt molecule called CPMAC enhances perovskite solar cell performance by 0.6%. CPMAC improves the electronic properties and reduces defects in the electron transfer layer compared to C60. CPMAC solar cells also exhibited greater stability, with a one-third reduction in power conversion efficiency drop compared to C60 cells under heat and humidity. Why it matters: This advancement addresses a key limitation in perovskite solar cell stability, potentially leading to more efficient and durable renewable energy solutions.
A recent report has identified the UAE as one of the top emerging economies in terms of Artificial Intelligence readiness. The report likely assesses various factors such as government policy, infrastructure development, talent acquisition, and investment in AI technologies within the country. This strong positioning underscores the UAE's proactive approach to integrating AI across sectors and achieving its national AI strategy goals. Why it matters: This highlights the UAE's successful strategic investments and policy frameworks aimed at establishing itself as a global leader in AI adoption and innovation.
Boston Consulting Group (BCG) has published a report titled "The GCC AI Pulse: Mapping the Region’s Readiness for an AI-Driven Future." The report assesses the current state of artificial intelligence adoption and preparedness across the Gulf Cooperation Council countries. It likely analyzes key areas such as infrastructure, talent, policy frameworks, and investment crucial for an AI-driven economy. Why it matters: This comprehensive assessment provides critical insights for policymakers, businesses, and researchers in the GCC to strategically plan and accelerate their AI development and integration.
The paper introduces MedNNS, a neural network search framework designed for medical imaging, addressing challenges in architecture selection and weight initialization. MedNNS constructs a meta-space encoding datasets and models based on their performance using a Supernetwork-based approach, expanding the model zoo size by 51x. The framework incorporates rank loss and Fréchet Inception Distance (FID) loss to capture inter-model and inter-dataset relationships, improving alignment in the meta-space and outperforming ImageNet pre-trained DL models and SOTA NAS methods.
McLaren Racing and KAUST are collaborating to advance research, innovation, and education in high-performance motorsport, supporting Saudi Vision 2030. McLaren driver Oscar Piastri visited KAUST for a research tour showcasing collaborative projects in aerodynamics, advanced lubricants, and biosensors. KAUST researchers are applying aerodynamics work with McLaren to support Saudi industry, and designing improved lubricant formulations to reduce friction. Why it matters: The partnership exemplifies cross-sector collaboration and KAUST's role in driving high-impact science and technology in the region, with applications extending beyond Formula 1.
KAUST and the National Center for Wildlife (NCW) discovered an unexpected ecosystem deep below the Farasan Bank coral reef system in the Red Sea. The deep waters were inhabited by corals, fish, and other animals thriving in low oxygen and high acidity conditions. Creatures showed coping strategies like slower swimming and healthy coral growth despite conditions normally preventing calcification. Why it matters: The discovery highlights the Red Sea's significance as a natural laboratory for studying marine resilience to climate change, expanding our understanding of how marine life adapts to extreme conditions.
KAUST researchers have discovered the first molecular events that trigger wheat's immunity to stem rust, a devastating fungal disease. The study, published in Science, identifies that tandem kinases are bound together and inactive until a pathogen binds, initiating an immune response that kills the infected cell. This prevents the pathogen from spreading and causing widespread crop damage. Why it matters: Understanding these molecular mechanisms could lead to engineering wheat with stronger and more durable resistance to stem rust and other diseases, safeguarding a crucial food source in the face of climate change and emerging pathogens.
KAUST's Professor Carlos M. Duarte has been awarded the Japan Prize for his research on blue carbon ecosystems and their role in climate solutions. Duarte's work has demonstrated that these ecosystems, though small, store a large percentage of carbon in marine sediments. He has also advised Saudi Arabia on marine conservation and influenced global ocean policy. Why it matters: This award highlights the importance of KAUST's contributions to sustainability research and marine conservation, aligning with Saudi Arabia's Vision 2030 and the Saudi Green Initiative.
This paper introduces SemDiff, a novel method for generating unrestricted adversarial examples (UAEs) by exploring the semantic latent space of diffusion models. SemDiff uses multi-attribute optimization to ensure attack success while preserving the naturalness and imperceptibility of generated UAEs. Experiments on high-resolution datasets demonstrate SemDiff's superior performance compared to state-of-the-art methods in attack success rate and imperceptibility, while also evading defenses.
KAUST and Aramco are partnering to develop extreme temperature-resilient batteries for subsurface applications and the hot climate of Saudi Arabia. The new batteries aim to operate effectively from subzero to 130°C, addressing the limitations of traditional lithium-based batteries that degrade above 60°C. KAUST is focusing on identifying electrolytes and separators that can endure high temperatures, while Aramco provides operational expertise. Why it matters: This collaboration could lead to significant cost savings in the energy sector, improve battery performance in EVs and grid systems, and position Saudi Arabia as a leader in advanced battery technology for extreme environments.
Researchers from MBZUAI introduced RP-SAM2, a method to improve surgical instrument segmentation by refining point prompts for more stable results. RP-SAM2 uses a novel shift block and compound loss function to reduce sensitivity to point prompt placement, improving segmentation accuracy in data-constrained settings. Experiments on the Cataract1k and CaDIS datasets show that RP-SAM2 enhances segmentation accuracy and reduces variance compared to SAM2, with code available on GitHub.
KAUST researchers have captured the initial unwinding of DNA using cryo-electron microscopy and deep learning. The study details 15 atomic states describing how the Simian Virus 40 Large Tumor Antigen helicase unwinds DNA, revealing the coordinated roles of DNA, helicases, and ATP. The research elucidates the fundamental mechanisms of DNA replication, a cornerstone of growth and reproduction. Why it matters: This detailed understanding of helicase function could lead to advances in nanotechnology and our understanding of genetic processes.
The paper introduces SaudiCulture, a new benchmark for evaluating the cultural competence of LLMs within Saudi Arabia, covering five major geographical regions and diverse cultural domains. The benchmark includes questions of varying complexity and distinguishes between common and specialized regional knowledge. Evaluations of five LLMs (GPT-4, Llama 3.3, FANAR, Jais, and AceGPT) revealed performance declines on region-specific questions, highlighting the need for region-specific knowledge in LLM training.
Researchers introduce SALT, a parameter-efficient fine-tuning method for medical image segmentation that combines singular value adaptation with low-rank transformation. SALT selectively adapts influential singular values and complements this with a low-rank update for the remaining subspace. Experiments on five medical datasets show SALT outperforms state-of-the-art PEFT methods by 2-5% in Dice score with only 3.9% trainable parameters.
The paper introduces UAE-3D, a multi-modal VAE for 3D molecule generation that compresses molecules into a unified latent space, maintaining near-zero reconstruction error. This approach simplifies latent diffusion modeling by eliminating the need to handle multi-modality and equivariance separately. Experiments on GEOM-Drugs and QM9 datasets show UAE-3D establishes new benchmarks in de novo and conditional 3D molecule generation, with significant improvements in efficiency and quality.
KAUST and KACST researchers have developed a nanoPE nanoplastic that improves LED streetlight energy efficiency by enhancing thermal radiation emission and reducing LED temperature. The nanoPE coating allows infrared light to pass through while reflecting visible light, optimizing illumination. Simulations suggest that adopting this technology in the US could reduce carbon dioxide emissions by over one million metric tons. Why it matters: This innovation offers a sustainable lighting solution with significant potential for reducing energy consumption and carbon emissions in Saudi Arabia and globally.
Researchers at MBZUAI have introduced a novel approach to enhance Large Multimodal Models (LMMs) for autonomous driving by integrating 3D tracking information. This method uses a track encoder to embed spatial and temporal data, enriching visual queries and improving the LMM's understanding of driving scenarios. Experiments on DriveLM-nuScenes and DriveLM-CARLA benchmarks demonstrate significant improvements in perception, planning, and prediction tasks compared to baseline models.
KAUST researchers are partnering with Saudi farmers and the Ministry of Environment, Water and Agriculture (MEWA) to develop tailored desalination solutions for agriculture. A new KAUST Center of Excellence project aims to integrate controlled environment agriculture (CEA) with desalination of non-conventional water resources for hydroponic farming. The approach focuses on selective ion removal to provide 'clean-enough' water, reducing energy use and costs compared to traditional desalination. Why it matters: This initiative could enable more sustainable and affordable local crop production in Saudi Arabia, potentially shifting the Kingdom from importing to exporting agricultural technologies.
KAUST researchers, in collaboration with KACST, discovered that dissolving nylon in battery electrolytes improves the performance of lithium-metal batteries. The nylon additive resulted in more efficient batteries with longer lifespans and fewer unwanted reactions. The research was published in ACS Energy Letters and Energy Environmental Science. Why it matters: This promises cheaper, safer, and more powerful lithium batteries for applications in electric vehicles and aviation, supporting Saudi Arabia's renewable energy goals.
KISTI (Korea Institute of Science and Technology Information) has announced the establishment of strategic partnerships with various institutions in Kuwait. These collaborations are specifically focused on advancing research and development in the fields of Artificial Intelligence and Data Science. The initiative aims to foster cooperation and knowledge exchange between South Korea and Kuwait in these critical technological areas. Why it matters: This partnership signifies a concerted effort to enhance AI and data science capabilities within Kuwait through international collaboration, potentially accelerating technological growth and innovation in the region.
KAUST is developing a robotic system for automated date palm harvesting, combining robotics and AI. The system uses robotic arms with visual sensors to identify and harvest dates, flowers, and tree structures. Field trials are scheduled for the 2025 harvest season, with full operational capability expected within three years. Why it matters: This innovation could transform Saudi Arabia's date farming industry, increasing yields, reducing labor risks, and positioning the country as a leader in agricultural technology.
This paper introduces the AI Pentad model, comprising humans/organizations, algorithms, data, computing, and energy, as a framework for AI regulation. It also presents the CHARME²D Model to link the AI Pentad with regulatory enablers like registration, monitoring, and enforcement. The paper assesses AI regulatory efforts in the EU, China, UAE, UK, and US using the CHARME²D model, highlighting strengths and weaknesses.
MBZUAI researchers introduce LLMVoX, a 30M-parameter, LLM-agnostic, autoregressive streaming text-to-speech (TTS) system that generates high-quality speech with low latency. The system preserves the capabilities of the base LLM and achieves a lower Word Error Rate compared to speech-enabled LLMs. LLMVoX supports seamless, infinite-length dialogues and generalizes to new languages with dataset adaptation, including Arabic.
A new survey paper provides a deep dive into post-training methodologies for Large Language Models (LLMs), analyzing their role in refining LLMs beyond pretraining. It addresses key challenges such as catastrophic forgetting, reward hacking, and inference-time trade-offs, and highlights emerging directions in model alignment, scalable adaptation, and inference-time reasoning. The paper also provides a public repository to continually track developments in this fast-evolving field.
A new culturally inclusive and linguistically diverse dataset called Palm for Arabic LLMs is introduced, covering 22 Arab countries and featuring instructions in both Modern Standard Arabic (MSA) and dialectal Arabic (DA) across 20 topics. The dataset was built through a year-long community-driven project involving 44 researchers from across the Arab world. Evaluation of frontier LLMs using the dataset reveals limitations in cultural and dialectal understanding, with some countries being better represented than others.
KAUST researchers have identified the gene 'CIROZ' as responsible for pediatric heart defects and misplacement of internal organs, working with institutes in Saudi Arabia and worldwide. The research examined samples from 16 patients from 10 families, including four from Saudi Arabia, revealing CIROZ's role in embryonic development symmetry. The findings provide insights into heritable diseases, which are more prevalent in Saudi Arabia. Why it matters: Identifying this gene allows for focused research on preventative strategies and curative therapies for congenital heart defects, particularly relevant in regions with higher rates of such diseases.
Researchers introduce a benchmark to evaluate the factual recall and knowledge transferability of multilingual language models across 13 languages. The study reveals that language models often fail to transfer knowledge between languages, even when they possess the correct information in one language. The benchmark and evaluation framework are released to drive future research in multilingual knowledge transfer.
Researchers introduce TimeTravel, a benchmark dataset for evaluating large multimodal models (LMMs) on historical and cultural artifacts. The benchmark comprises 10,250 expert-verified samples across 266 cultures and 10 historical regions, designed to assess AI in tasks like classification and interpretation of manuscripts, artworks, inscriptions, and archaeological discoveries. The goal is to establish AI as a reliable partner in preserving cultural heritage and assisting researchers.
A new dataset called ArabCulture is introduced to address the lack of culturally relevant commonsense reasoning resources in Arabic AI. The dataset covers 13 countries across the Gulf, Levant, North Africa, and the Nile Valley, spanning 12 daily life domains with 54 fine-grained subtopics. It was built from scratch by native speakers writing and validating culturally relevant questions. Why it matters: The dataset highlights the need for more culturally aware models and benchmarks tailored to the Arabic-speaking world, moving beyond machine-translated resources.
The paper introduces MultiProSE, the first multi-label Arabic dataset for propaganda, sentiment, and emotion detection. It extends the existing ArPro dataset with sentiment and emotion annotations, resulting in 8,000 annotated news articles. Baseline models, including GPT-4o-mini and BERT-based models, were developed for each task, and the dataset, guidelines, and code are publicly available. Why it matters: This resource enables further research into Arabic language models and a better understanding of opinion dynamics within Arabic news media.
UAE President Sheikh Mohamed bin Zayed Al Nahyan visited the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) in a high-profile tour. During his visit, he interacted with students and faculty, emphasizing the critical role of AI education and research for the nation's future development. The visit underscored the UAE's significant commitment to advancing its capabilities and talent pool in the field of artificial intelligence. Why it matters: This direct engagement from the UAE's top leadership highlights the strategic importance of MBZUAI as a cornerstone of the country's national AI agenda and its efforts to foster a knowledge-based economy.
KAUST researchers have discovered that a coral's resilience to rising temperatures is determined by the microorganisms living inside them. The study identifies specific combinations of microeukaryotes and bacteria that enhance heat resistance in corals. This finding provides valuable clues for developing coral probiotics to protect and restore coastal reefs. Why it matters: This breakthrough could lead to effective interventions to combat coral bleaching and preserve vital marine ecosystems in the Red Sea and beyond.
This research evaluates LLMs like ChatGPT, Llama, Aya, Jais, and ACEGPT on Arabic automated essay scoring (AES) using the AR-AES dataset. The study uses zero-shot, few-shot learning, and fine-tuning approaches while using a mixed-language prompting strategy. ACEGPT performed best among the LLMs with a QWK of 0.67, while a smaller BERT model achieved 0.88. Why it matters: The study highlights challenges faced by LLMs in processing Arabic and provides insights into improving LLM performance in Arabic NLP tasks.
The Oil Sustainability Program (OSP), in partnership with PIF, SIRC, NEOM, SIKA, and ClimateCrete, has launched the "NovusCrete" Consortium to innovate in sustainable concrete solutions. The initiative prioritizes localization using materials like seawater, recycled construction waste, and fine sand, supported by entities like the American Concrete Institute (ACI). ClimateCrete, a KAUST spin-off, uses patented technology to transform fine sand into high-quality concrete sand, reducing cement use and CO2 emissions by up to 60%. Why it matters: The consortium and ClimateCrete's technology represent significant steps towards sustainable construction practices in the region, aligning with Saudi Arabia's carbon neutrality goals and fostering a deep-tech startup ecosystem.
This paper introduces Adaptive Entropy-aware Optimization (AEO), a new framework to tackle Multimodal Open-set Test-time Adaptation (MM-OSTTA). AEO uses Unknown-aware Adaptive Entropy Optimization (UAE) and Adaptive Modality Prediction Discrepancy Optimization (AMP) to distinguish unknown class samples during online adaptation by amplifying the entropy difference between known and unknown samples. The study establishes a new benchmark derived from existing datasets with five modalities and evaluates AEO's performance across various domain shift scenarios, demonstrating its effectiveness in long-term and continual MM-OSTTA settings.
KAUST's Ibn Sina Distinguished Professor Carlos Duarte has been awarded the prestigious Japan Prize for his pioneering research on blue carbon and marine ecosystems. Duarte's work demonstrated the significance of marine ecosystems like seagrasses, mangroves, and salt marshes in sequestering and storing carbon dioxide. He coined the term 'blue carbon' and has advised Vision2030 initiatives. Why it matters: This award recognizes the importance of KAUST's research in marine conservation and highlights Saudi Arabia's growing role in ocean-based climate change solutions.
Hamad Bin Khalifa University's Qatar Computing Research Institute (QCRI) introduced Fanar, an Arabic-centric multimodal generative AI platform featuring the Fanar Star (7B) and Fanar Prime (9B) Arabic LLMs. These models were trained on nearly 1 trillion tokens and are designed to address different prompts through a custom orchestrator. Fanar includes a customized Islamic RAG system, a Recency RAG, bilingual speech recognition, and an attribution service for content verification, sponsored by Qatar's Ministry of Communications and Information Technology. Why it matters: The platform signifies a major step towards sovereign AI development in Qatar, providing advanced Arabic language capabilities and addressing regional needs.
KAUST researchers have developed a new technology for direct lithium extraction from brine in oilfields and seawater, potentially positioning Saudi Arabia as a major lithium producer. The technology, demonstrated at a pilot scale, allows extraction from low-concentration sources (as low as 20 parts per million) without pollutants. It could increase global lithium resources from 22 million tons to over 230 billion tons. Why it matters: This innovation could transform Saudi Arabia from a lithium importer to a producer, strengthening its energy security and influence in the global clean energy market.
The paper introduces VENOM, a text-driven framework for generating high-quality unrestricted adversarial examples using diffusion models. VENOM unifies image content generation and adversarial synthesis into a single reverse diffusion process, enhancing both attack success rate and image quality. The framework incorporates an adaptive adversarial guidance strategy with momentum to ensure the generated adversarial examples align with the distribution of natural images.
KAUST researchers, partnering with the Queensland Government, discovered 34 previously unknown seagrass meadows in the Red Sea by tracking the foraging behavior of green turtles. They tagged and tracked 53 turtles, revealing that the turtles were approximately 20 times more reliable at identifying seagrass meadows compared to the Allen Coral Atlas. This method also proved to be significantly more cost-effective than traditional methods like airplanes with hyperspectral sensors. Why it matters: This study highlights a novel, cost-effective approach to mapping blue carbon ecosystems, crucial for carbon capture and marine habitat preservation, and provides valuable data for sustainability policies in the Red Sea region.
KAUST, in partnership with MCIT and Hello Tomorrow, has released a report outlining a roadmap for Saudi Arabia to become a global deep tech hub. The report focuses on five pillars: ecosystem, investment, talent, infrastructure, and policy, highlighting the role of R&D in achieving Vision 2030. Deep tech startups in Saudi Arabia raised over $100 million between 2020 and 2022, with a 75% increase in deep tech researchers since 2015. Why it matters: This signals Saudi Arabia's increasing commitment to fostering a local deep tech ecosystem and attracting investment in advanced technologies.
Enowa and KAUST held the Enowa-KAUST Energy Summit 2024, celebrating the third year of their Energy Cortex Program. The Energy Cortex Program funds applied research for clean energy solutions, focusing on renewable energy technologies led by KAUST faculty. The program is structured around Weatherlytics, GenFlex Cortex, Stor Cortex, and Grid Cortex, and has engaged KAUST professors, produced six journal papers, and provided NEOM with data. Why it matters: This partnership aims to revolutionize renewable energy in Saudi Arabia by integrating AI and advanced data analytics to optimize energy generation and distribution, supporting the Kingdom's sustainable energy goals.
This paper introduces AraLLaMA, a new Arabic large language model (LLM) trained using a progressive vocabulary expansion method inspired by second language acquisition. The model utilizes a modified byte-pair encoding (BPE) algorithm to dynamically extend the Arabic subwords in its vocabulary during training, balancing the out-of-vocabulary (OOV) ratio. Experiments show AraLLaMA achieves performance comparable to existing Arabic LLMs on various benchmarks, and all models, data, and code will be open-sourced. Why it matters: This work addresses the need for more accessible and performant Arabic LLMs, contributing to democratization of AI in the Arab world.
Researchers at MBZUAI have demonstrated a method called "Data Laundering" to artificially boost language model benchmark scores using knowledge distillation. The technique covertly transfers benchmark-specific knowledge, leading to inflated accuracy without genuine improvements in reasoning. The study highlights a vulnerability in current AI evaluation practices and calls for more robust benchmarks.
MBZUAI researchers introduce UniMed-CLIP, a unified Vision-Language Model (VLM) for diverse medical imaging modalities, trained on the new large-scale, open-source UniMed dataset. UniMed comprises over 5.3 million image-text pairs across six modalities: X-ray, CT, MRI, Ultrasound, Pathology, and Fundus, created using LLMs to transform classification datasets into image-text formats. UniMed-CLIP significantly outperforms existing generalist VLMs and matches modality-specific medical VLMs in zero-shot evaluations, improving over BiomedCLIP by +12.61 on average across 21 datasets while using 3x less training data.
KAUST participated in the 16th Conference of the Parties (COP16) to the Convention to Combat Desertification (UNCCD) in Riyadh, showcasing its sustainability innovations. KAUST and the Ministry of Environment, Water and Agriculture (MEWA) announced the launch of a new international water-research center to be headquartered at KAUST. KAUST also entered a SAR100 million agreement with the National Center for Palm and Dates (NCPD) for date-palm sector research. Why it matters: These initiatives highlight KAUST's commitment to advancing Saudi Arabia’s data-driven “green” efforts beyond 2030 and addressing critical environmental issues.