This study addresses the lack of structured causal modeling between tactical strike behavior and strategic delay in current strategic-level simulations, particularly the structural bottlenecks in capturing intermediate variables within the "resilience - nodal suppression - negotiation window" chain. We propose the Intervention-Aware Spatio-Temporal Graph Neural Network (IA-STGNN), a novel framework that closes the causal loop from tactical input to strategic delay output. The model integrates graph attention mechanisms, counterfactual simulation units, and spatial intervention node reconstruction to enable dynamic simulations of strike configurations and synchronization strategies. Training data are generated from a multi-physics simulation platform (GEANT4 + COMSOL) under NIST SP 800-160 standards, ensuring structural traceability and policy-level validation. Experimental results demonstrate that IA-STGNN significantly outperforms baseline models (ST-GNN, GCN-LSTM, XGBoost), achieving a 12.8 percent reduction in MAE and 18.4 percent increase in Top-5 percent accuracy, while improving causal path consistency and intervention stability. IA-STGNN enables interpretable prediction of strategic delay and supports applications such as nuclear deterrence simulation, diplomatic window assessment, and multi-strategy optimization, providing a structured and transparent AI decision-support mechanism for high-level policy modeling.

--The remarkable success of transformers across various fields such as natural language processing and computer vision has paved the way for their applications in automatic modulation classification, a critical component in the communication systems of Internet of Things (IoT) devices. However, it has been observed that transformer-based classification of radio signals is susceptible to subtle yet sophisticated adversarial attacks. T o address this issue, we have developed a defensive strategy for transformer-based modulation classification systems to counter such adversarial attacks. In this paper, we propose a novel vision transformer (ViT) architecture by introducing a new concept known as adversarial indicator (AdvI) token to detect adversarial attacks. T o the best of our knowledge, this is the first work to propose an AdvI token in ViT to defend against adversarial attacks. Integrating an adversarial training method with a detection mechanism using AdvI token, we combine a training time defense and running time defense in a unified neural network model, which reduces architectural complexity of the system compared to detecting adversarial perturbations using separate models. We investigate into the operational principles of our method by examining the attention mechanism. We show the proposed AdvI token acts as a crucial element within the ViT, influencing attention weights and thereby highlighting regions or features in the input data that are potentially suspicious or anomalous. Through experimental results, we demonstrate that our approach surpasses several competitive methods in handling white-box attack scenarios, including those utilizing the fast gradient method, projected gradient descent attacks and basic iterative method. Lu Zhang is with School of Mathematics and Computer Science, Swansea university, Swansea, SA1 8EN, UK (e-mail: lu.zhang@swansea.ac.uk). Sangarapillai Lambotharan is with Institute for Digital Technologies, Loughborough University London, London, E20 3BS, UK (e-mail: s.lambotharan@lboro.ac.uk). Gan Zheng is with School of Engineering, University of Warwick, Coventry, CV4 7AL, UK (e-mail: gan.zheng@warwick.ac.uk). Guisheng Liao is with School of Electronic Engineering, Xidian University, Xi'an, 710071, People's Republic of China (e-mail: liaogs@xidian.edu.cn). Xuekang Liu is with the Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, London, SW7 2AZ, U.K. (e-mail: xuekangliu@ieee.org).

Persistence is often regarded as a virtue in science. In this paper, however, we challenge this conventional view by highlighting its contextual nature, particularly how persistence can become a liability during periods of paradigm shift. We focus on the deep learning revolution catalyzed by AlexNet in 2012. Analyzing the 20-year career trajectories of over 5,000 scientists who were active in top machine learning venues during the preceding decade, we examine how their research focus and output evolved. We first uncover a dynamic period in which leading venues increasingly prioritized cutting-edge deep learning developments that displaced relatively traditional statistical learning methods. Scientists responded to these changes in markedly different ways. Those who were previously successful or affiliated with old teams adapted more slowly, experiencing what we term a rigidity penalty - a reluctance to embrace new directions leading to a decline in scientific impact, as measured by citation percentile rank. In contrast, scientists who pursued strategic adaptation - selectively pivoting toward emerging trends while preserving weak connections to prior expertise - reaped the greatest benefits. Taken together, our macro- and micro-level findings show that scientific breakthroughs act as mechanisms that reconfigure power structures within a field.

Human communication involves a complex interplay of verbal and nonverbal signals, essential for conveying meaning and achieving interpersonal goals. To develop socially intelligent AI technologies, it is crucial to develop models that can both comprehend and generate dyadic behavioral dynamics. To this end, we introduce the Seamless Interaction Dataset, a large-scale collection of over 4,000 hours of face-to-face interaction footage from over 4,000 participants in diverse contexts. This dataset enables the development of AI technologies that understand dyadic embodied dynamics, unlocking breakthroughs in virtual agents, telepresence experiences, and multimodal content analysis tools. We also develop a suite of models that utilize the dataset to generate dyadic motion gestures and facial expressions aligned with human speech. These models can take as input both the speech and visual behavior of their interlocutors. We present a variant with speech from an LLM model and integrations with 2D and 3D rendering methods, bringing us closer to interactive virtual agents. Additionally, we describe controllable variants of our motion models that can adapt emotional responses and expressivity levels, as well as generating more semantically-relevant gestures. Finally, we discuss methods for assessing the quality of these dyadic motion models, which are demonstrating the potential for more intuitive and responsive human-AI interactions.

Rapidly improving AI capabilities and autonomy hold significant promise of transformation, but are also driving vigorous debate on how to ensure that AI is safe, i.e., trustworthy, reliable, and secure. Building a trusted ecosystem is therefore essential -- it helps people embrace AI with confidence and gives maximal space for innovation while avoiding backlash. The "2025 Singapore Conference on AI (SCAI): International Scientific Exchange on AI Safety" aimed to support research in this space by bringing together AI scientists across geographies to identify and synthesise research priorities in AI safety. This resulting report builds on the International AI Safety Report chaired by Yoshua Bengio and backed by 33 governments. By adopting a defence-in-depth model, this report organises AI safety research domains into three types: challenges with creating trustworthy AI systems (Development), challenges with evaluating their risks (Assessment), and challenges with monitoring and intervening after deployment (Control).

A pro-Russia disinformation campaign is leveraging consumer artificial intelligence tools to fuel a "content explosion" focused on exacerbating existing tensions around global elections, Ukraine, and immigration, among other controversial issues, according to new research published last week. The campaign, known by many names including Operation Overload and Matryoshka (other researchers have also tied it to Storm-1679), has been operating since 2023 and has been aligned with the Russian government by multiple groups, including Microsoft and the Institute for Strategic Dialogue. While the campaign targets audiences around the world, including in the US, its main target has been Ukraine. Hundreds of AI-manipulated videos from the campaign have tried to fuel pro-Russian narratives. The report outlines how, between September 2024 and May 2025, the amount of content being produced by those running the campaign has increased dramatically and is receiving millions of views around the world.

In the predawn darkness of June 13, Israel launched a "preemptive" attack on Iran. Explosions rocked various parts of the country. Among the targets were nuclear sites at Natanz and Fordo, military bases, research labs, and senior military residences. By the end of the operation, Israel had killed at least 974 people while Iranian missile strikes in retaliation had killed 28 people in Israel. Israel described its actions as anticipatory self-defence, claiming Iran was mere weeks away from producing a functional nuclear weapon.

The United Nations special rapporteur on the situation of human rights in the occupied Palestinian territory (oPt) has released a new report mapping the corporations aiding Israel in the displacement of Palestinians and its genocidal war on Gaza, in breach of international law. Francesca Albanese's latest report, which is scheduled to be presented at a news conference in Geneva on Thursday, names 48 corporate actors, including United States tech giants Microsoft, Alphabet Inc. – Google's parent company – and Amazon. A database of more than 1000 corporate entities was also put together as part of the investigation. "[Israel's] forever-occupation has become the ideal testing ground for arms manufacturers and Big Tech – providing significant supply and demand, little oversight, and zero accountability – while investors and private and public institutions profit freely," the report said. "Companies are no longer merely implicated in occupation – they may be embedded in an economy of genocide," it said, in a reference to Israel's ongoing assault on the Gaza Strip.

After more than a day of continuous debate, the US Senate passed its version of the budget megabill Tuesday afternoon--with potentially disastrous implications for the future of renewable energy in the country. The bill ends credits for projects placed in service--a term meaning, essentially, that a project is ready to provide power to the grid--after 2027, putting hundreds of planned projects around the country in jeopardy. "This is a bill to punish renewables," says Costa Samaras, a professor of civil and environmental engineering at Carnegie Mellon University. "There is a real need to add clean energy supply to the grid--electrifying our cars, electrifying our homes, electrifying our buildings, electrifying our factories, and the demands from AI are all going to require new clean energy. What this bill does is make it harder and more expensive."

A Ukrainian drone attack at an industrial plant in central Russia has killed three people and injured 35 others, a Russian regional governor has said. Alexander Brechalov, head of the Udmurt Republic, said in a post on Telegram on Tuesday that the attack took place at a factory in Izhevsk city. Ten of the wounded were in a serious condition, he noted. There was no immediate official comment from Kyiv. But a Ukrainian security official confirmed the attack, telling the news agency Reuters that the Kupol plant had been hit, with a fire breaking out as a result.