Measuring low-frequency electric fields remains difficult when traceability, small size, and vector resolution are all required at the same time. A team at Nanyang Technological University, Singapore, proposed a sensing scheme based on a Rydberg dipolar atom chain, where the external field is reflected in angle-dependent many-body interactions. With readouts in the time, energy, and frequency domains, the work suggests a feasible way to realize compact and vector-resolved sensing of low-frequency electric fields.

A University of Manchester Professor has been appointed by  Lord Vallance, Minister of State for Science, Innovation, Research and Nuclear, as an Expert Reviewer for an independent assessment of the Nuclear Decommissioning Authority (NDA);  an executive non-departmental public body that is charged with, on behalf of government, the mission to clean-up the UK’s earliest nuclear sites safely, securely and cost effectively.

Beijing, China — Researchers from Beijing Institute of Technology and other leading institutions have developed a novel approach for improving Multi-Hop Question Answering (MHQA) tasks, which require models to reason over multiple relevant facts to answer complex questions. The new method, called CausalBridgeQA, integrates causal inference into the MHQA process to address persistent issues of reasoning breakdowns and feature spurious correlations, which are common challenges in current models.

Data is often referred to as the new oil of the digital economy, representing a highly valuable and untapped asset. To fully realize the potential of spatial data, various spatial data marketplace platforms have emerged. The existing spatial data marketplaces primarily focus on recommending each dataset individually. There is a lack of consideration for cases where an individual dataset cannot satisfy the buyer’s needs such that a collection of datasets needs to be acquired.

Researchers from BUPT introduce the RFGDG framework,utilizing RL to dynamically optimize graph generalization in federated settings.

Prompt engineering has emerged as a critical tool to refine AI outputs, but existing techniques are fragmented and lack a cohesive structure. The research team, led by Professor Feng Zhang from Renmin University of China and collaborators from Microsoft AI, Tsinghua University, and the National University of Singapore, published their new research on 15 March 2026 in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature.

Promoting the development and application of more accurate, ethically responsible artificial intelligence tools adapted to real clinical needs in order to improve the quality of healthcare is one of the main objectives of the NursIA network, an initiative led by a multidisciplinary team at the Universitat Jaume I in Castelló. The network brings together five research groups in fields such as nursing, biostatistics, machine learning, process mining and applied ethics, with the aim of contributing to the transformation of healthcare through innovative AI-based solutions that improve patient safety and the efficiency of healthcare processes.

Funded by the Universitat Jaume I’s 2025–2026 programme for the promotion of research and knowledge transfer, the network aims to generate impact in three key areas: scientific and technical development, institutional collaboration and knowledge transfer, and communication and outreach. Planned activities include a conference on the practical implementation of AI in clinical environments, the first open NursIA conference on artificial intelligence and healthcare transformation, and several scientific publications on AI applied to healthcare.

More than seventy years after Alan Turing posed the question of whether machines could think, advances in artificial intelligence have shifted the focus from imitation to collaboration. As AI increasingly undertakes complex academic tasks through multi-agent collaboration, a new question emerges: beyond the Turing Test, how should the human role in research be redefined?