We present the challenging task of automatically creating a high-level Wikipedia-style article that aggregates information from multiple diverse videos about real-world events, such as natural disasters or political elections. Videos are intuitive sources for retrieval-augmented generation (RAG), but most contemporary RAG workflows focus heavily on text and existing methods for video-based summarization focus on low-level scene understanding rather than high-level event semantics. To close this gap, we introduce WikiVideo, a benchmark consisting of expert-written articles and densely annotated videos that provide evidence for articles' claims, facilitating the integration of video into RAG pipelines and enabling the creation of in-depth content that is grounded in multimodal sources. We further propose Collaborative Article Generation (CAG), a novel interactive method for article creation from multiple videos. CAG leverages an iterative interaction between an r1-style reasoning model and a VideoLLM to draw higher level inferences about the target event than is possible with VideoLLMs alone, which fixate on low-level visual features. We benchmark state-of-the-art VideoLLMs and CAG in both oracle retrieval and RAG settings and find that CAG consistently outperforms alternative methods, while suggesting intriguing avenues for future work.

Selective retrieval improves retrieval-augmented generation (RAG) by reducing distractions from low-quality retrievals and improving efficiency. However, existing approaches under-utilize the inherent knowledge of large language models (LLMs), leading to suboptimal retrieval decisions and degraded generation performance. To bridge this gap, we propose Self-Routing RAG (SR-RAG), a novel framework that binds selective retrieval with knowledge verbalization. SR-RAG enables an LLM to dynamically decide between external retrieval and verbalizing its own parametric knowledge. To this end, we design a multi-task objective that jointly optimizes an LLM on knowledge source selection, knowledge verbalization, and response generation. We further introduce dynamic knowledge source inference via nearest neighbor search to improve the accuracy of knowledge source decision under domain shifts. Fine-tuning three LLMs with SR-RAG significantly improves both their response accuracy and inference latency. Compared to the strongest selective retrieval baseline, SR-RAG reduces retrievals by 29% while improving the performance by 5.1%.

A Russian drone attack has killed at least four people and wounded 21 in the eastern Ukrainian city of Dnipro, damaging high-rise buildings and triggering fires in a hotel and homes, the regional governor said, as Moscow claims to have made gains on the ground elsewhere. Late Friday, Russia sent "more than two dozen drones" to Dnipro, the governor of the surrounding Dnipropetrovsk region, Sergiy Lysak, wrote on his official Telegram account on Saturday. "The massive attack caused large-scale destruction and fires. A hotel and restaurant complex, 11 private houses, garages, and a service station were on fire," he said, adding that high-rises and cars were also damaged. Pictures and videos posted online showed flames and large plumes of smoke wafting skyward.

Overnight, air sirens were heard sounding in several other Ukrainian regions, including the capital Kyiv. It was not immediately clear whether there were any casualties. The Russian military has not commented on the issue. In his video address late on Friday, Ukrainian President Volodymyr Zelensky again accused Russia of targeting Ukrainian energy infrastructure - in violation of a temporary moratorium agreed earlier this month in talks involving the US. Moscow has also repeatedly blamed Ukraine for attacking Russia's energy sector. Russian President Vladimir Putin earlier this week suggested that Ukraine should temporarily be placed under UN control to elect what he called a more "competent" government.

On March 18, the official White House account on X posted two photographs of Virginia Basora-Gonzalez, a woman who was arrested earlier this month by U.S. Immigration and Customs Enforcement. The post described her as a "previously deported alien felon convicted of fentanyl trafficking," and celebrated her capture as a win for the administration. In one photograph, Basora-Gonzalez is shown handcuffed and weeping in a public parking lot. The White House account posted about Basora-Gonzalez again yesterday--this time, rendering her capture in the animated style of the beloved Japanese filmmaker Hayao Miyazaki, who co-founded the animation company Studio Ghibli. Presumably, whoever runs the account had used ChatGPT, which has been going viral this week for an update to its advanced "4o" model that enables it to transform photographs in the style of popular art, among other things.

Oceanic processes at fine scales are crucial yet difficult to observe accurately due to limitations in satellite and in-situ measurements. The Surface Water and Ocean Topography (SWOT) mission provides high-resolution Sea Surface Height (SSH) data, though noise patterns often obscure fine scale structures. Current methods struggle with noisy data or require extensive supervised training, limiting their effectiveness on real-world observations. We introduce SIMPGEN (Simulation-Informed Metric and Prior for Generative Ensemble Networks), an unsupervised adversarial learning framework combining real SWOT observations with simulated reference data. SIMPGEN leverages wavelet-informed neural metrics to distinguish noisy from clean fields, guiding realistic SSH reconstructions. Applied to SWOT data, SIMPGEN effectively removes noise, preserving fine-scale features better than existing neural methods. This robust, unsupervised approach not only improves SWOT SSH data interpretation but also demonstrates strong potential for broader oceanographic applications, including data assimilation and super-resolution.

The accurate navigation of autonomous underwater vehicles critically depends on the precision of Doppler velocity log (DVL) velocity measurements. Recent advancements in deep learning have demonstrated significant potential in improving DVL outputs by leveraging spatiotemporal dependencies across multiple sensor modalities. However, integrating these estimates into model-based filters, such as the extended Kalman filter, introduces statistical inconsistencies, most notably, cross-correlations between process and measurement noise. This paper addresses this challenge by proposing a cross-correlation-aware deep INS/DVL fusion framework. Building upon BeamsNet, a convolutional neural network designed to estimate AUV velocity using DVL and inertial data, we integrate its output into a navigation filter that explicitly accounts for the cross-correlation induced between the noise sources. This approach improves filter consistency and better reflects the underlying sensor error structure. Evaluated on two real-world underwater trajectories, the proposed method outperforms both least squares and cross-correlation-neglecting approaches in terms of state uncertainty. Notably, improvements exceed 10% in velocity and misalignment angle confidence metrics. Beyond demonstrating empirical performance, this framework provides a theoretically principled mechanism for embedding deep learning outputs within stochastic filters.

Large language models afford opportunities for using computers for intensive tasks, realizing research opportunities that have not been considered before. One such opportunity could be a systematic interrogation of the scientific literature. Here, we show how a large language model can be used to construct a literature review of 2699 publications associated with microphysics parametrizations in the Weather and Research Forecasting (WRF) model, with the goal of learning how they were used and their systematic biases, when simulating precipitation. The database was constructed of publications identified from Web of Science and Scopus searches. The large language model GPT-4 Turbo was used to extract information about model configurations and performance from the text of 2699 publications. Our results reveal the landscape of how nine of the most popular microphysics parameterizations have been used around the world: Lin, Ferrier, WRF Single-Moment, Goddard Cumulus Ensemble, Morrison, Thompson, and WRF Double-Moment. More studies used one-moment parameterizations before 2020 and two-moment parameterizations after 2020. Seven out of nine parameterizations tended to overestimate precipitation. However, systematic biases of parameterizations differed in various regions. Except simulations using the Lin, Ferrier, and Goddard parameterizations that tended to underestimate precipitation over almost all locations, the remaining six parameterizations tended to overestimate, particularly over China, southeast Asia, western United States, and central Africa. This method could be used by other researchers to help understand how the increasingly massive body of scientific literature can be harnessed through the power of artificial intelligence to solve their research problems.

Its evolution is governed by intricate air-sea interactions, posing significant challenges for long-term prediction. In this study, we introduce CTEFNet, a multivariate deep learning model that synergizes convolutional neural networks and transformers to enhance ENSO forecasting. By integrating multiple oceanic and atmospheric predictors, CTEFNet extends the effective forecast lead time to 20 months while mitigating the impact of the spring predictability barrier, outperforming both dynamical models and state-of-the-art deep learning approaches. Furthermore, CTEFNet offers physically meaningful and statistically significant insights through gradient-based sensitivity analysis, revealing the key precursor signals that govern ENSO dynamics, which align with well-established theories and reveal new insights about inter-basin interactions among the Pacific, Atlantic, and Indian Oceans. The CTEFNet's superior predictive skill and interpretable sensitivity assessments underscore its potential for advancing climate prediction. Our findings highlight the importance of multivariate coupling in ENSO evolution and demonstrate the promise of deep learning in capturing complex climate dynamics with enhanced interpretability. 1 Introduction El Ni no-Southern Oscillation (ENSO) is one of the most prominent modes of inter-annual climate variability, characterized by shifts in sea surface temperatures (SST) across the tropical Pacific Ocean and the weakening of equatorial trade winds.

United States officials are set to meet with their Ukrainian counterparts again after a round of talks with Russian negotiators on a partial ceasefire in Ukraine. A senior Ukrainian official told the AFP news agency that the meeting would be held later on Monday after US and Russian delegations wrap up their day's talks in Saudi Arabia's capital Riyadh. Monday's US-Russia talks were primarily focused on ending attacks on Black Sea shipping, with a view to ushering in a broader ceasefire agreement that would bring an end to the three-year Russia-Ukraine war. US officials had already met the Ukrainian team on Sunday to discuss the protection of civilian and energy infrastructure, said Ukrainian Defence Minister Rustem Umerov, who led the delegation and called the talks "productive". Reporting from Kyiv, Al Jazeera's Assed Baig said Ukraine was now keen to see Russia agree to a deal that would protect Black Sea shipping, particularly "the cessation of shelling of Ukrainian ports Odesa, Kherson and Mykolaiv".