General AI research publications from MBZUAI, KAUST, TII, QCRI, and other GCC institutions, including conference papers, journal articles, and preprints.
KAUST, the National Livestock and Fisheries Development Program (NLFDP), and the National Research and Development Center for Sustainable Agriculture (Estidama) are collaborating to explore algae-based biostimulants for agriculture. These biostimulants, derived from marine algae, enhance plant growth and nutrient uptake without the negative impacts of chemical fertilizers. KAUST already operates a commercial-scale algae manufacturing plant capable of producing tons of algae per month for biostimulant production. Why it matters: This initiative positions Saudi Arabia as a leader in sustainable food technology by leveraging algae biostimulants to improve soil health and reduce dependence on imported raw materials.
Saudi Arabia has announced the establishment of a new International Water Research Center in partnership between the Ministry of Environment, Water and Agriculture and KAUST. The center will serve as a global platform for applied water research, addressing water economics, security, pollution, and digital monitoring. Headquartered at KAUST, the center seeks to foster national and international cooperation, leveraging KAUST's resources to develop sustainable solutions for water challenges. Why it matters: This initiative signals Saudi Arabia's commitment to addressing critical water challenges and solidifying its leadership in water research and technology within the region.
KAUST and the National Center for Palm and Dates (NCPD) have entered a SR100 million agreement for research projects in the date palm sector. The agreement aims to improve production efficiency, develop innovative agricultural practices, and mitigate economic risks to palm trees. KAUST will leverage its expertise to create a genetic atlas for Saudi date varieties and adopt sustainable management practices. Why it matters: This investment highlights Saudi Arabia's commitment to its cultural heritage and economic diversification through advancements in a key agricultural sector.
Researchers propose a spatio-temporal model for high-resolution wind forecasting in Saudi Arabia using Echo State Networks and stochastic partial differential equations. The model reduces spatial information via energy distance, captures dynamics with a sparse recurrent neural network, and reconstructs data using a non-stationary stochastic partial differential equation approach. The model achieves more accurate forecasts of wind speed and energy, potentially saving up to one million dollars annually compared to existing models.
MBZUAI releases BiMediX2, a bilingual (Arabic-English) Bio-Medical Large Multimodal Model, along with the BiMed-V dataset (1.6M samples) and BiMed-MBench evaluation benchmark. BiMediX2 supports multi-turn conversation in Arabic and English and handles diverse medical imaging modalities. The model achieves state-of-the-art results on medical LLM and LMM benchmarks, outperforming existing methods and GPT-4 in specific evaluations.
The paper introduces Arabic Stable LM, a 1.6B parameter Arabic-centric language model, in both base and chat versions. The Arabic Stable LM 1.6B chat model achieves strong results on several benchmarks, outperforming models with up to 8x more parameters. The study also demonstrates the benefit of incorporating synthetic instruction tuning data through a large synthetic dialogue dataset. Why it matters: This work makes Arabic LLMs more accessible by reducing the parameter size while maintaining strong performance, facilitating deployment in resource-constrained environments.
Terraxy, a KAUST startup, is providing solutions to transform Saudi deserts into fertile landscapes using CarboSoil, an advanced biochar product. CarboSoil improves soil fertility, conserves water, and enhances plant growth, and is tailored to counteract the alkaline nature of Saudi sandy soils. Terraxy is working with Saudi Aramco and NEOM to implement its technologies, servicing over 60,000 native plants. Why it matters: This innovation directly supports the Saudi Green Initiative and offers a sustainable approach to combat desertification and promote food production in the region.
KAUST has been awarded the ACM Gordon Bell Prize for Climate Modelling, considered the "Nobel" of high-performance computing, for their work on exascale climate emulators. The winning paper, a collaborative effort with institutions including the NSF National Center for Atmospheric Research, addresses the computational and storage demands of high-resolution earth system models. The KAUST team included Sameh Abdulah, Marc G. Genton, David E. Keyes, and others. Why it matters: This is the first time an institution in the Middle East has won the prize, highlighting KAUST's leadership in high-performance computing and climate research in the region.
A new study introduces Sporo AraSum, a language model designed for Arabic clinical documentation, and compares it to JAIS using synthetic datasets and modified PDQI-9 metrics. Sporo AraSum significantly outperformed JAIS in quantitative AI metrics and qualitative attributes related to accuracy, utility, and cultural competence. The model addresses the nuances of Arabic while reducing AI hallucinations, making it suitable for Arabic-speaking healthcare. Why it matters: The model offers a more culturally and linguistically sensitive solution for Arabic clinical documentation, potentially improving healthcare workflows and patient outcomes in the region.
Saudi Electricity Company (SEC) and KAUST have launched a pilot study at SEC’s Rabigh power plant to demonstrate a cryogenic technology that captures multiple pollutants and greenhouse gases, including carbon dioxide. The technology captures over 98% of carbon dioxide from flue gas, as well as sulfur dioxide, nitrogen oxides, and particulate matter, using a single system, unlike current technologies. The streamlined post-processing has a smaller environmental footprint and lower costs. Why it matters: This project supports Saudi Arabia's net-zero carbon goals and offers a potentially more efficient and cost-effective method for retrofitting existing power plants.
KAUST and MEWA's Aquaculture Development Program (ADP) showcased achievements at the 6th International Saudi Aquaculture Development Workshop. New fish nutrition formulations developed by KAUST Beacon Development (KBD) could save Saudi Arabia $417 million per year in aquaculture production costs by 2030 through improved feed conversion ratios. KBD has also established complete production cycles for Sobaity and Gilthead seabream under Red Sea conditions. Why it matters: These advancements boost Saudi Arabia's food security and promote sustainable aquaculture, reducing reliance on imports and diversifying the economy in line with Vision 2030.
A new study uses the UNet++ deep learning model and Sentinel-2 satellite data to monitor mangrove dynamics in the UAE from 2017 to 2024. The model achieved a mean Intersection over Union (mIoU) of 87.8% on the validation set. Results indicate a significant increase in mangrove area, primarily in Abu Dhabi, contributing to enhanced carbon sequestration across the UAE.
Researchers introduce Swan, a family of Arabic-centric embedding models including Swan-Small (based on ARBERTv2) and Swan-Large (based on ArMistral). They also propose ArabicMTEB, a benchmark suite for cross-lingual, multi-dialectal Arabic text embedding performance across 8 tasks and 94 datasets. Swan-Large achieves state-of-the-art results, outperforming Multilingual-E5-large in most Arabic tasks. Why it matters: The new models and benchmarks address a critical need for high-quality Arabic language models that are both dialectally and culturally aware, enabling more effective NLP applications in the region.
Researchers have introduced LlamaLens, a specialized multilingual LLM designed for analyzing news and social media content. The model addresses domain specificity and multilinguality, with a focus on news and social media in Arabic, English, and Hindi. LlamaLens was evaluated on 18 tasks represented by 52 datasets, outperforming the state-of-the-art on 23 testing sets. Why it matters: This work contributes a valuable resource for multilingual NLP research, particularly in the context of analyzing news and social media content across diverse languages.
A novel agent-based framework called FIRE is introduced for fact-checking long-form text. FIRE iteratively integrates evidence retrieval and claim verification, deciding whether to provide a final answer or generate a subsequent search query. Experiments show FIRE achieves comparable performance to existing methods while reducing LLM costs by 7.6x and search costs by 16.5x.
KAUST researchers have discovered biological clues in the Wahbah Crater in Saudi Arabia that could provide insights into the possibility of life on Enceladus, one of Saturn's moons. The researchers isolated 48 bacterial strains from the crater, identifying two with an adaptability suitable for the extreme environment of Enceladus. These strains thrive in high temperatures, salinity, and alkaline pH levels, mimicking conditions on the Saturn moon. Why it matters: This study highlights the potential of Saudi Arabia's extreme environments as valuable models for detecting extraterrestrial life and strengthens the country's growing interest in space exploration.
KAUST researchers have developed a passive cooling device that uses gravity and radiative cooling to extract water from the atmosphere without electricity. The device reflects thermal energy back to the sky while collecting water using gravity and a lubricant coating to eliminate water droplet pinning. Tested in Thuwal, Saudi Arabia, the system nearly doubled the water collection rate compared to other atmospheric water harvesting technologies. Why it matters: This innovation could improve the efficiency and adoption of solar cells in arid regions by providing a sustainable, electricity-free cooling and water harvesting solution.
KAUST and Oxford Nanopore Technologies have signed an MoU to collaborate on multi-omics research, building on previous work such as the NanoRanger technique developed by KAUST's Mo Li. KAUST will gain early access to Oxford Nanopore’s sequencing technology, while Oxford Nanopore will access KAUST's Core Labs. Why it matters: This partnership enhances KAUST's research capabilities in areas like rare diseases and desert agriculture, and provides Oxford Nanopore with a launchpad to engage with Saudi Arabia's research community.
Researchers developed Atlas-Chat, a collection of LLMs for dialectal Arabic, focusing on Moroccan Arabic (Darija). They constructed an instruction dataset by consolidating existing Darija language resources and translating English instructions. Atlas-Chat models (2B, 9B, 27B) outperform state-of-the-art and Arabic-specialized LLMs like LLaMa, Jais, and AceGPT on Darija NLP tasks. Why it matters: This work addresses the gap in LLM support for low-resource Arabic dialects, providing a methodology for instruction-tuning and benchmarks for future research.
KAUST, Abdul Latif Jameel Motors, and Toyota have formed a partnership to advance hydrogen fuel cell research in Saudi Arabia, supporting the Kingdom's decarbonization goals. KAUST has acquired proton electrolyte membrane (PEM) fuel cell modules from Toyota to establish a dedicated laboratory under its Clean Energy and Research Platform (CERP). Researchers at KAUST are conducting studies to optimize PEM fuel cells for Saudi Arabia's environmental conditions, aiming to maximize efficiency and reliability. Why it matters: This collaboration supports Saudi Arabia's Vision 2030 and net-zero goals, fostering innovation in hydrogen technology and sustainable energy solutions within the region.
The paper introduces Juhaina, a 9.24B parameter Arabic-English bilingual LLM trained with an 8,192 token context window. It identifies limitations in the Open Arabic LLM Leaderboard (OALL) and proposes a new benchmark, CamelEval, for more comprehensive evaluation. Juhaina outperforms models like Llama and Gemma in generating helpful Arabic responses and understanding cultural nuances. Why it matters: This culturally-aligned LLM and associated benchmark could significantly advance Arabic NLP and democratize AI access for Arabic speakers.
Researchers introduce AraDiCE, a benchmark for Arabic Dialect and Cultural Evaluation, comprising seven synthetic datasets in various dialects and Modern Standard Arabic (MSA). The benchmark includes approximately 45,000 post-edited samples and evaluates LLMs on dialect comprehension, generation, and cultural awareness across the Gulf, Egypt, and Levant. Results show that Arabic-specific models like Jais and AceGPT outperform multilingual models on dialectal tasks, but challenges remain in dialect identification, generation, and translation. Why it matters: This benchmark and associated datasets will help improve LLMs' ability to understand and generate diverse Arabic dialects and cultural contexts, addressing a significant gap in current models.
Aramco and KAUST-incubated startup Lihytech are collaborating to develop Direct Lithium Extraction (DLE) technology in Saudi Arabia. Aramco is providing oilfield brines to Lihytech to assess their lithium extraction technology at KAUST Research and Technology Park. The collaboration supports Saudi Arabia's Vision 2030 and its growing demand for lithium in electric vehicles. Why it matters: This partnership could unlock a new critical mineral industry in Saudi Arabia, leveraging existing oilfield resources for sustainable lithium production.
KAUST has launched a Center of Excellence (CoE) for Renewable Energy and Storage Technologies, chaired by Professor Husam Alshareef, to develop and deploy innovative renewable energy and storage technologies. The center will focus on homegrown technologies invented at KAUST and aims to attract investments, partnerships, and market interest for commercial adoption. Research will focus on prototyping and scaling technologies across six key themes including advanced photovoltaics and new battery chemistries. Why it matters: This initiative aligns with Saudi Vision 2030, supporting the Kingdom's climate goals and transition to sustainable energy while creating jobs and enhancing energy security.
KAUST has launched four Centers of Excellence (CoEs) focusing on Health, Sustainable Environment, Energy, and Economies of the Future, aligning with Saudi Arabia’s Vision 2030. One CoE, chaired by Professor Bernard Ghanem and co-chaired by Professor Juergen Schmidhuber, will focus on generative AI. These centers aim to deliver impactful solutions that directly contribute to national economic objectives. Why it matters: This initiative signifies a major push towards applied AI research and development within Saudi Arabia, particularly in generative AI, renewable energy, food security, and smart health.
KAUST has established a Center of Excellence (CoE) for Generative AI, chaired by Professor Bernard Ghanem and co-chaired by Professor Jürgen Schmidhuber. The center will focus on scientific research, commercial innovation, and talent development in GenAI, aligning with Saudi Arabia's Vision 2030 goals. The CoE aims to impact Saudi Arabia's four RDI priorities: Health and Wellness, Sustainable Environment, Energy and Industrial Leadership, and Economies of the Future. Why it matters: The KAUST center aims to position Saudi Arabia as a global leader in generative AI, addressing the need for specialized expertise and infrastructure while contributing to the Kingdom's economic diversification.
KAUST has launched a Center of Excellence for Sustainable Food Security, led by Professor Mark Tester. The center aims to develop innovative solutions for food security challenges in arid regions, aligning with Saudi Vision 2030. It will focus on enhancing resource use efficiency, developing resilient crops, and promoting sustainable biosystems through interdisciplinary research and partnerships. Why it matters: This initiative will advance agricultural innovation in Saudi Arabia, supporting economic diversification and reducing reliance on food imports.
KAUST researchers have identified a gene, CLAMT1b, in pearl millet that affects its vulnerability to the parasitic weed Striga hermonthica. Pearl millet strains lacking CLAMT1b were found to be resistant to the weed, while those expressing the gene were susceptible. The gene's presence leads to the secretion of strigolactones, promoting interaction with Striga, but its absence does not harm symbiotic relationships with beneficial fungi. Why it matters: This discovery offers new breeding strategies to enhance pearl millet's resistance to parasitic weeds, bolstering food security in arid regions like Saudi Arabia and Africa where the crop is vital.
KAUST researchers have developed a parameter-efficient learning approach to identify Arabic dialects using limited data and computing power, fine-tuning the Whisper model with a dataset of 17 dialects. The model achieves high accuracy using only 2.5% of the parameters of the larger model and 30% of the training data. Srijith Radhakrishnan presented the findings at EMNLP 2023 and Interspeech 2023. Why it matters: This research addresses the challenge of dialect identification in Arabic NLP and enables more efficient use of large language models in resource-constrained environments.
KAUST, NEOM’s Education, Research, and Innovation Foundation (ERIF), and ENOWA have formed a partnership to support Saudi Arabia’s hydrogen economy. ERIF has sponsored three strategic projects under its Hydrogen and e-Fuels Applied Research Institute (HEFARI) with KAUST researchers focusing on developing hydrogen as a renewable energy vector. The projects cover carbon-neutral fuels, cost-effective electrolyzer technologies, and lowering emissions from green ammonia. Why it matters: This collaboration aims to establish Saudi Arabia as a leader in green hydrogen technologies and sustainable fuel production, aligning with the Kingdom's decarbonization goals.
KAUST researchers have developed a genomic resource for Tausch’s goatgrass (Aegilops tauschii), a wild relative of wheat, by creating 46 high-quality genome assemblies. They compiled 493 genetically distinct accessions from an initial 900, collaborating with the Open Wild Wheat Consortium to select accessions with traits of interest, such as disease resistance and stress tolerance. Screening these assemblies helped identify rust resistance genes, including mapping a stem rust resistance gene to the Sr33 locus. Why it matters: This genomic resource will accelerate gene discovery in wheat, potentially improving modern wheat varieties and enhancing global food security.
KAUST researchers have demonstrated that incorporating tetrahydrotriazinium into perovskite/silicon tandem solar cells enhances both performance and stability. The additive increases hydrogen bonds in the perovskite film's crystal structure, improving power conversion efficiency to 33.7% and phase stability during testing under intense conditions. The improved cells showed more stability after 1500 hours of testing, modeling harsh environments. Why it matters: This research offers a pathway to more durable and efficient solar cells suitable for deployment in harsh climates like the Arabian Peninsula, potentially boosting renewable energy adoption in the region.
Researchers introduce ArabLegalEval, a multitask benchmark dataset for assessing Arabic legal knowledge in LLMs. The dataset contains tasks sourced from Saudi legal documents and synthesized questions, drawing inspiration from MMLU and LegalBench. Experiments benchmarked models including GPT-4 and Jais, exploring in-context learning and various evaluation methods. Why it matters: This resource should help accelerate AI research and evaluation in the Arabic legal domain, where datasets are lacking.
KAUST has commissioned Freire Shipyards to build the RV Thuwal II, Saudi Arabia's first regional class research vessel, expected to be completed in 2026. The vessel will be 50m long and designed for a 30-year lifespan, with modular labs and green propulsion technologies. It will support marine research in the Red Sea, accommodate 30 people, and aid in emergency response. Why it matters: This investment enhances Saudi Arabia's research capabilities in marine science and positions KAUST as a hub for Red Sea exploration and international scientific collaboration.
FancyVideo, a new video generator, introduces a Cross-frame Textual Guidance Module (CTGM) to enhance text-to-video models. CTGM uses a Temporal Information Injector and Temporal Affinity Refiner to achieve frame-specific textual guidance, improving comprehension of temporal logic. Experiments on the EvalCrafter benchmark demonstrate FancyVideo's state-of-the-art performance in generating dynamic and consistent videos, also supporting image-to-video tasks.
This paper introduces two methods for creating Arabic LLM prompts at scale: translating existing English prompt datasets and creating natural language prompts from Arabic NLP datasets. Using these methods, the authors generated over 67.4 million Arabic prompts covering tasks like summarization and question answering. Fine-tuning a 7B Qwen2 model on these prompts outperforms a 70B Llama3 model in handling Arabic prompts. Why it matters: The research provides a cost-effective approach to scaling Arabic LLM training data, potentially improving the performance of smaller, more accessible models for Arabic NLP.
MBZUAI researchers release OpenFactCheck, a unified framework to evaluate the factual accuracy of large language models. The framework includes modules for response evaluation, LLM evaluation, and fact-checker evaluation. OpenFactCheck is available as an open-source Python library, a web service, and via GitHub.
KAUST researchers have been selected as finalists for two ACM Gordon Bell Prizes for high-performance computing. One project used NVIDIA GPUs to enhance genetic studies from the UK Biobank, achieving 133x speedup over existing software. The other developed an exascale climate emulator with higher spatial-temporal resolution than current models, demonstrated on supercomputers like Shaheen III. Why it matters: The recognition highlights KAUST's strength in high-performance computing research and its contributions to both genetic analysis and climate modeling.
KAUST and KFSHRC have developed NanoRanger, a new gene sequencing system for identifying mutations causing genetic diseases. NanoRanger offers a faster and simpler process to detect DNA abnormalities at base resolution, building on existing long-read sequencing technologies. The system is designed to be cheaper and faster, targeting diseases prevalent in Saudi Arabia due to consanguinity. Why it matters: The technology has the potential to improve diagnosis and treatment of Mendelian diseases, which are especially prevalent in the Arab world.
This paper introduces a nested embedding learning framework for Arabic NLP, utilizing Matryoshka Embedding Learning and multilingual models. The authors translated sentence similarity datasets into Arabic to enable comprehensive evaluation. Experiments on the Arabic Natural Language Inference dataset show Matryoshka embedding models outperform traditional models by 20-25% in capturing Arabic semantic nuances. Why it matters: This work advances Arabic NLP by providing a new method and evaluation benchmark for semantic similarity, which is crucial for tasks like information retrieval and text understanding.
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.
The paper introduces AlcLaM, an Arabic dialectal language model trained on 3.4M sentences from social media. AlcLaM expands the vocabulary and retrains a BERT-based model, using only 13GB of dialectal text. Despite the smaller training data, AlcLaM outperforms models like CAMeL, MARBERT, and ArBERT on various Arabic NLP tasks. Why it matters: AlcLaM offers a more efficient and accurate approach to Arabic NLP by focusing on dialectal Arabic, which is often underrepresented in existing models.
Ericsson is continuing its funding for two telecommunications programs at KAUST, managed by Professors Mohamed-Slim Alouini and Atif Shamim, focusing on free-space optics (FSO) and reconfigurable intelligent surfaces (RIS). These technologies are considered critical for achieving 5G and 6G capabilities. FSO uses lasers to transmit signals through free space, while RIS develops intelligent surfaces to manage wireless signals. Why it matters: This partnership positions Saudi Arabia at the forefront of developing next-generation telecommunications infrastructure and capabilities, addressing key challenges in 5G and 6G deployment.
The paper introduces NativQA, a language-independent framework for constructing culturally and regionally aligned QA datasets in native languages. Using the framework, the authors created MultiNativQA, a multilingual natural QA dataset consisting of ~64k manually annotated QA pairs in seven languages. The dataset covers queries from native speakers from 9 regions covering 18 topics, and is designed for evaluating and tuning LLMs. Why it matters: The framework and dataset enable the creation of more culturally relevant and effective LLMs for diverse linguistic communities, including those in the Middle East.
KAUST and Partanna have launched a 12-month R&D partnership to enhance CO2 removal in concrete manufacturing. The collaboration will integrate Partanna’s formula with KAUST’s Direct Air Capture (DAC) technology. Partanna's patented concrete avoids Portland Cement and uses a binder made from natural and recycled materials, enabling CO2 absorption. Why it matters: This partnership highlights Saudi Arabia's commitment to sustainable construction and carbon-negative technologies, potentially revolutionizing building practices in the region and beyond.
The paper introduces InstAr-500k, a new Arabic instruction dataset of 500,000 examples designed to improve LLM performance in Arabic. Researchers fine-tuned the open-source Gemma-7B model using InstAr-500k and evaluated it on downstream tasks, achieving strong results on Arabic NLP benchmarks. They then released GemmAr-7B-V1, a model specifically tuned for Arabic NLP tasks. Why it matters: This work addresses the lack of high-quality Arabic instruction data, potentially boosting the capabilities of Arabic language models.
This paper introduces Diffusion-BBO, a new online black-box optimization (BBO) framework that uses a conditional diffusion model as an inverse surrogate model. The framework employs an Uncertainty-aware Exploration (UaE) acquisition function to propose scores in the objective space for conditional sampling. The approach is shown theoretically to achieve a near-optimal solution and empirically outperforms existing online BBO baselines across 6 scientific discovery tasks.
MBZUAI researchers introduce Web2Code, a new large-scale dataset and evaluation framework for training and benchmarking multimodal LLMs on webpage understanding and HTML code generation. The dataset includes webpage images, HTML code, and QA pairs about webpage content. Experiments demonstrate the dataset's utility in webpage understanding, code generation, and general visual domain tasks, with code and data available on Github.
Researchers introduce two new benchmarks, derived from the Qiyas exam, to evaluate mathematical reasoning and language understanding in Arabic. They tested ChatGPT-3.5-turbo and ChatGPT-4, which achieved 49% and 64% accuracy respectively. The new benchmarks aim to address the lack of resources for evaluating Arabic language models.
MBZUAI researchers introduce VideoGPT+, a novel video Large Multimodal Model (LMM) that integrates image and video encoders to leverage both spatial and temporal information in videos. They also introduce VCGBench-Diverse, a comprehensive benchmark for evaluating video LMMs across 18 video categories. VideoGPT+ demonstrates improved performance on multiple video benchmarks, including VCGBench and MVBench.