Special Envoy to the Middle East Steve Witkoff tells'Hannity' what's next in Russia-Ukraine peace talks after President Donald Trump's phone call with Russian President Vladimir Putin. Peace talks between U.S. and Russian delegations aimed at ending the war in Ukraine are underway Monday in Saudi Arabia, according to media reports. The discussions come after Ukrainian President Volodymyr Zelenskyy said a delegation from his country had a "quite useful" meeting with an American team in Riyadh on Sunday. "Our team is working in a fully constructive manner, and the discussion is quite useful. The work of delegations continues. But no matter what we're discussing with our partners right now, Putin must be pushed to issue a real order to stop the strikes – because the one who brought this war must be the one to take it back," Zelenskyy said.

Visual Question Answering (VQA) requires reasoning across visual and textual modalities, yet Large Vision-Language Models (LVLMs) often lack integrated commonsense knowledge, limiting their robustness in real-world scenarios. To address this, we introduce MAGIC-VQA, a novel framework that enhances VQA by systematically integrating commonsense knowledge with LVLMs. MAGIC-VQA employs a three-stage process: (1) Explicit Knowledge Integration from external sources, (2) By-Type Post-Processing for contextual refinement, and (3) Implicit Knowledge Augmentation using a Graph Neural Network (GNN) for structured reasoning. While GNNs bring greater depth to structured inference, they enable superior relational inference beyond LVLMs. MAGIC-VQA bridges a key gap by unifying commonsensse knowledge with LVLM-driven reasoning, eliminating the need for extensive pre-training or complex prompt tuning. Our framework achieves state-of-the-art performance on benchmark datasets, significantly improving commonsense reasoning in VQA.

Air pollution has become a major threat to human health, making accurate forecasting crucial for pollution control. Traditional physics-based models forecast global air pollution by coupling meteorology and pollution processes, using either online or offline methods depending on whether fully integrated with meteorological models and run simultaneously. However, the high computational demands of both methods severely limit real-time prediction efficiency. Existing deep learning (DL) solutions employ online coupling strategies for global air pollution forecasting, which finetune pollution forecasting based on pretrained atmospheric models, requiring substantial training resources. This study pioneers a DL-based offline coupling framework that utilizes bilinear pooling to achieve offline coupling between meteorological fields and pollutants. The proposed model requires only 13% of the parameters of DL-based online coupling models while achieving competitive performance. Compared with the state-of-the-art global air pollution forecasting model CAMS, our approach demonstrates superiority in 63% variables across all forecast time steps and 85% variables in predictions exceeding 48 hours. This work pioneers experimental validation of the effectiveness of meteorological fields in DL-based global air pollution forecasting, demonstrating that offline coupling meteorological fields with pollutants can achieve a 15% relative reduction in RMSE across all pollution variables. The research establishes a new paradigm for real-time global air pollution warning systems and delivers critical technical support for developing more efficient and comprehensive AI-powered global atmospheric forecasting frameworks.

"I feel that he (Putin) wants peace," said President Trump's personal envoy Steve Witkoff, adding: "I think that you're going to see in Saudi Arabia on Monday some real progress." Yet Dmitry Peskov, the Kremlin spokesman has dampened expectations. "We are only at the beginning of this path," he told Russian state TV. Kyiv suffered one of its heaviest attacks from Russian drones on Saturday night, with three people killed, including a five-year-old girl. "We need to push Putin to give a real order to stop the strikes," said Ukraine's President Volodymyr Zelensky in his evening address on Sunday.

President Donald Trump's envoy to Russia and Ukraine says he doesn't believe Russian President Vladimir Putin wants to invade Europe. Envoy Steve Witkoff made the statement during a Sunday morning appearance on "Fox News Sunday," commenting on Putin's motives on a "larger scale." "Now I've been asked my opinion about what President Putin's motives are on a larger scale. And I simply have said that I just don't see that he wants to take all of Europe," Witkoff said. "This is a much different situation than it was in World War II. There was no NATO," he added.

At least seven people have been killed in overnight Russian drone attacks on the Ukrainian capital, as President Volodymyr Zelenskyy urged his Western allies to put more pressure on Moscow to cease its attacks on the country in advance of peace talks in Saudi Arabia. Three people, including a five-year-old, were killed and 10 were injured in a drone attack on Kyiv, the city's military administration said on Sunday. Elsewhere, four people were killed in Russian attacks in Donetsk region, regional Governor Vadym Filashkin said, including three who died in an attack on the front-line Ukrainian town of Dobropillya. Kyiv Mayor Vitali Klitschko wrote on Telegram that emergency services were dispatched to several city districts following fires and damage. Earlier, the country's air force said Russia launched 147 drones overnight on several Ukrainian regions.

Sea ice plays a critical role in the global climate system and maritime operations, making timely and accurate classification essential. However, traditional manual methods are time-consuming, costly, and have inherent biases. Automating sea ice type classification addresses these challenges by enabling faster, more consistent, and scalable analysis. While both traditional and deep learning approaches have been explored, deep learning models offer a promising direction for improving efficiency and consistency in sea ice classification. However, the absence of a standardized benchmark and comparative study prevents a clear consensus on the best-performing models. To bridge this gap, we introduce \textit{IceBench}, a comprehensive benchmarking framework for sea ice type classification. Our key contributions are threefold: First, we establish the IceBench benchmarking framework which leverages the existing AI4Arctic Sea Ice Challenge dataset as a standardized dataset, incorporates a comprehensive set of evaluation metrics, and includes representative models from the entire spectrum of sea ice type classification methods categorized in two distinct groups, namely, pixel-based classification methods and patch-based classification methods. IceBench is open-source and allows for convenient integration and evaluation of other sea ice type classification methods; hence, facilitating comparative evaluation of new methods and improving reproducibility in the field. Second, we conduct an in-depth comparative study on representative models to assess their strengths and limitations, providing insights for both practitioners and researchers. Third, we leverage IceBench for systematic experiments addressing key research questions on model transferability across seasons (time) and locations (space), data downscaling, and preprocessing strategies.

Understanding the relationship between textual news and time-series evolution is a critical yet under-explored challenge in applied data science. While multimodal learning has gained traction, existing multimodal time-series datasets fall short in evaluating cross-modal reasoning and complex question answering, which are essential for capturing complex interactions between narrative information and temporal patterns. To bridge this gap, we introduce Multimodal Time Series Benchmark (MTBench), a large-scale benchmark designed to evaluate large language models (LLMs) on time series and text understanding across financial and weather domains. MTbench comprises paired time series and textual data, including financial news with corresponding stock price movements and weather reports aligned with historical temperature records. Unlike existing benchmarks that focus on isolated modalities, MTbench provides a comprehensive testbed for models to jointly reason over structured numerical trends and unstructured textual narratives. The richness of MTbench enables formulation of diverse tasks that require a deep understanding of both text and time-series data, including time-series forecasting, semantic and technical trend analysis, and news-driven question answering (QA). These tasks target the model's ability to capture temporal dependencies, extract key insights from textual context, and integrate cross-modal information. We evaluate state-of-the-art LLMs on MTbench, analyzing their effectiveness in modeling the complex relationships between news narratives and temporal patterns. Our findings reveal significant challenges in current models, including difficulties in capturing long-term dependencies, interpreting causality in financial and weather trends, and effectively fusing multimodal information.

Autonomous underwater vehicles (AUVs) are essential for various applications, including oceanographic surveys, underwater mapping, and infrastructure inspections. Accurate and robust navigation are critical to completing these tasks. To this end, a Doppler velocity log (DVL) and inertial sensors are fused together. Recently, a model-based approach demonstrated the ability to extract the vehicle acceleration vector from DVL velocity measurements. Motivated by this advancement, in this paper we present an end-to-end deep learning approach to estimate the AUV acceleration vector based on past DVL velocity measurements. Based on recorded data from sea experiments, we demonstrate that the proposed method improves acceleration vector estimation by more than 65% compared to the model-based approach by using data-driven techniques. As a result of our data-driven approach, we can enhance navigation accuracy and reliability in AUV applications, contributing to more efficient and effective underwater missions through improved accuracy and reliability.

After nine months stuck on the International Space Station (ISS), NASA's Butch Wilmore and Suni Williams finally made it back home last night. The duo splashed down off the coast of Florida aboard SpaceX's Crew Dragon capsule, having arrived at the ISS way back in June. While Wilmore and Williams will be relieved to be back on solid ground, their return has ignited a slew of conspiracy theories - with many sceptics critical of the splashdown in particular. Upon arrival, the capsule was circled by an inquisitive pod of dolphins, which many social media commentators are describing as'fake' and computer-generated. Others have taken it even further, suggesting the entire mission footage from departure to landing was created by a sophisticated AI tool.