At least three people have been killed in a Russian drone attack near Ukraine's capital. The interior ministry said on Friday that drone debris killed two men and a woman in the overnight attack on the central Kyiv region, damaging a residential apartment building, eight houses, commercial buildings and several cars. The attack came as Russian authorities said the country's air defence systems intercepted and destroyed 121 drones launched by Ukraine overnight. The drones were downed over 13 Russian regions, including seven over Moscow and the nearby region, the defence ministry said in a statement on Telegram. Moscow Mayor Sergei Sobyanin said the drones had been intercepted at several locations around the capital.

Russian aerial attacks in eastern and central Ukraine killed at least three people and wounded dozens. Among those killed were a 53-year-old in the Kostiantynivka area and a 54-year-old in the northeastern Kharkiv region. The mayor of Russia's Ryazan region, Sergei Sobyanin, said air defence units intercepted three Ukrainian drones headed for Russia's capital Moscow. No damage or casualties were reported, but specialist emergency crews were deployed to the site. Ukraine ordered the evacuation of some 267 children and their families from 16 settlements along the front line in the east of the country that were under threat from advancing Russian forces.

President Donald Trump signed an executive order on artificial intelligence Thursday that will revoke past government policies his order says "act as barriers to American AI innovation." To maintain global leadership in AI technology, "we must develop AI systems that are free from ideological bias or engineered social agendas," Trump's order says. The new order doesn't name which existing policies are hindering AI development but sets out to track down and review "all policies, directives, regulations, orders, and other actions taken" as a result of former President Joe Biden's sweeping AI executive order of 2023, which Trump rescinded Monday. Any of those Biden-era actions must be suspended if they don't fit Trump's new directive that AI should "promote human flourishing, economic competitiveness, and national security." Last year, the Biden administration issued a policy directive that said U.S. federal agencies must show their artificial intelligence tools aren't harming the public, or stop using them. Trump's order directs the White House to revise and reissue those directives, which affect how agencies acquire AI tools and use them.

Red teaming has emerged as a critical practice in assessing the possible risks of AI models and systems. It aids in the discovery of novel risks, stress testing possible gaps in existing mitigations, enriching existing quantitative safety metrics, facilitating the creation of new safety measurements, and enhancing public trust and the legitimacy of AI risk assessments. This white paper describes OpenAI's work to date in external red teaming and draws some more general conclusions from this work. We describe the design considerations underpinning external red teaming, which include: selecting composition of red team, deciding on access levels, and providing guidance required to conduct red teaming. Additionally, we show outcomes red teaming can enable such as input into risk assessment and automated evaluations. We also describe the limitations of external red teaming, and how it can fit into a broader range of AI model and system evaluations. Through these contributions, we hope that AI developers and deployers, evaluation creators, and policymakers will be able to better design red teaming campaigns and get a deeper look into how external red teaming can fit into model deployment and evaluation processes. These methods are evolving and the value of different methods continues to shift as the ecosystem around red teaming matures and models themselves improve as tools for red teaming.

Algorithmic modelling relies on limited information in data to extrapolate outcomes for unseen scenarios, often embedding an element of arbitrariness in its decisions. A perspective on this arbitrariness that has recently gained interest is multiplicity-the study of arbitrariness across a set of "good models", i.e., those likely to be deployed in practice. In this work, we systemize the literature on multiplicity by: (a) formalizing the terminology around model design choices and their contribution to arbitrariness, (b) expanding the definition of multiplicity to incorporate underrepresented forms beyond just predictions and explanations, (c) clarifying the distinction between multiplicity and other traditional lenses of arbitrariness, i.e., uncertainty and variance, and (d) distilling the benefits and potential risks of multiplicity into overarching trends, situating it within the broader landscape of responsible AI. We conclude by identifying open research questions and highlighting emerging trends in this young but rapidly growing area of research.

We aim to redefine robust ego-motion estimation and photorealistic 3D reconstruction by addressing a critical limitation: the reliance on noise-free data in existing models. While such sanitized conditions simplify evaluation, they fail to capture the unpredictable, noisy complexities of real-world environments. Dynamic motion, sensor imperfections, and synchronization perturbations lead to sharp performance declines when these models are deployed in practice, revealing an urgent need for frameworks that embrace and excel under real-world noise. To bridge this gap, we tackle three core challenges: scalable data generation, comprehensive benchmarking, and model robustness enhancement. First, we introduce a scalable noisy data synthesis pipeline that generates diverse datasets simulating complex motion, sensor imperfections, and synchronization errors. Second, we leverage this pipeline to create Robust-Ego3D, a benchmark rigorously designed to expose noise-induced performance degradation, highlighting the limitations of current learning-based methods in ego-motion accuracy and 3D reconstruction quality. Third, we propose Correspondence-guided Gaussian Splatting (CorrGS), a novel test-time adaptation method that progressively refines an internal clean 3D representation by aligning noisy observations with rendered RGB-D frames from clean 3D map, enhancing geometric alignment and appearance restoration through visual correspondence. Extensive experiments on synthetic and real-world data demonstrate that CorrGS consistently outperforms prior state-of-the-art methods, particularly in scenarios involving rapid motion and dynamic illumination.

We present an end-to-end workflow for superconducting qubit readout that embeds co-designed Neural Networks (NNs) into the Quantum Instrumentation Control Kit (QICK). Capitalizing on the custom firmware and software of the QICK platform, which is built on Xilinx RFSoC FPGAs, we aim to leverage machine learning (ML) to address critical challenges in qubit readout accuracy and scalability. The workflow utilizes the hls4ml package and employs quantization-aware training to translate ML models into hardware-efficient FPGA implementations via user-friendly Python APIs. We experimentally demonstrate the design, optimization, and integration of an ML algorithm for single transmon qubit readout, achieving 96% single-shot fidelity with a latency of 32ns and less than 16% FPGA look-up table resource utilization. Our results offer the community an accessible workflow to advance ML-driven readout and adaptive control in quantum information processing applications.

Objective: This study proposes a Natural Language Processing pipeline to evaluate prescription appropriateness in Italian referrals, where reasons for prescriptions are recorded only as free text, complicating automated comparisons with guidelines. The pipeline aims to derive, for the first time, a comprehensive summary of the reasons behind these referrals and a quantification of their appropriateness. While demonstrated in a specific case study, the approach is designed to generalize to other types of examinations. Methods: Leveraging embeddings from a transformer-based model, the proposed approach clusters referral texts, maps clusters to labels, and aligns these labels with existing guidelines. We present a case study on a dataset of 496,971 referrals, consisting of all referrals for venous echocolordopplers of the lower limbs between 2019 and 2021 in the Lombardy Region. A sample of 1,000 referrals was manually annotated to validate the results. Results: The pipeline exhibited high performance for referrals' reasons (Prec=92.43%, Rec=83.28%) and excellent results for referrals' appropriateness (Prec=93.58%, Rec=91.52%) on the annotated subset. Analysis of the entire dataset identified clusters matching guideline-defined reasons - both appropriate and inappropriate - as well as clusters not addressed in the guidelines. Overall, 34.32% of referrals were marked as appropriate, 34.07% inappropriate, 14.37% likely inappropriate, and 17.24% could not be mapped to guidelines. Conclusions: The proposed pipeline effectively assessed prescription appropriateness across a large dataset, serving as a valuable tool for health authorities. Findings have informed the Lombardy Region's efforts to strengthen recommendations and reduce the burden of inappropriate referrals.

Recent advances in table understanding have focused on instruction-tuning large language models (LLMs) for table-related tasks. However, existing research has overlooked the impact of hyperparameter choices and lacks a comprehensive evaluation of the out-of-domain table understanding ability and the general capabilities of these table LLMs. In this paper, we evaluate these abilities in existing table LLMs, and reveal significant declines in both out-of-domain table understanding and general capabilities compared to their base models. Through systematic analysis, we show that hyperparameters, such as learning rate, can significantly influence both table-specific and general capabilities. Contrary to the existing table instruction-tuning works, we demonstrate that smaller learning rates and fewer training instances can enhance table understanding while preserving general capabilities. Based on our findings, we introduce TAMA, a TAble LLM instruction-tuned from LLaMA 3.1 8B Instruct, which achieves performance on par with, or surpassing GPT-3.5 and GPT-4 on table tasks, while maintaining strong out-of-domain generalization and general capabilities. Our findings highlight the potential for reduced data annotation costs and more efficient model development through careful hyperparameter selection.

The rainfall-runoff relationship is a fundamental concept in hydrology. It describes how rainfall is transformed into surface runoff through interconnected hydrologic processes, such as infiltration, evapotranspiration, and the exchange of water between surface and subsurface flows (Beven & Kirkby, 1979). Thoroughly understanding these hydrologic processes and subsequently achieving accurate simulations of the rainfall-runoff relationship are critical for proactive flood forecasting and mitigation, efficient agricultural planning, and strategic urban development (Beven, 2012; Knapp et al., 1991; Moradkhani & Sorooshian, 2008). Physically-based hydrologic models (PBMs), grounded in physical laws that govern hydrologic dynamics, are the standard tools for simulating rainfall-runoff relationships (Beven, 1996). However, the highly nonlinear nature of various hydrologic processes often challenges PBMs, limiting their accuracy in diverse conditions (Beven, 1989; Clark et al., 2017). Consequently, there is a growing need for innovative approaches to address the limitations of PBMs. Deep learning (DL) has emerged as an alternative to PBMs, showing success in capturing the complex, nonlinear patterns in rainfall-runoff simulations. The hydrology community also explores the applicability of DL models in rainfall-runoff simulations across diverse temporal scales and geospatial locations.