VILNIUS/STOCKHOLM/LONDON - Ukrainian drones have strayed into Baltic countries' airspace in recent weeks, sowing confusion and raising tensions with Russia at a time when U.S. commitment to NATO's collective security is in question. The airspace incursions have occurred as Ukraine, seeking to land heavier blows on Russia four years after Moscow's full-scale invasion, uses exploding drones to hit Russian Baltic ports that handle nearly 40% of national oil and gas exports. In most cases, Kyiv and the Baltic states have confirmed the stray drones are Ukrainian but have blamed Russia for causing them to deviate from their flight path with the use of electronic defenses that jam or spoof signals. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

Microsimulation models used by ministries of finance and central banks rely on parametric processes for lifetime earnings that capture only first and second moments of the conditional distribution and miss long-range nonlinear structure. We propose SAGA, a decoder-only transformer for irregular tabular panel sequences, paired with a split conformal calibration wrapper that delivers individual-level prediction intervals with finite-sample marginal coverage guarantees. Trained on the longitudinal Swedish LISA register over 1990 to 2022, comprising 2,143,817 individuals and 61,284,903 person-years, the model forecasts annual labor earnings at horizons of one to thirty years and aggregates them by Monte Carlo into present-discounted lifetime earnings distributions. Against the canonical Guvenen, Karahan, Ozkan, and Song parametric process and tabular and recurrent baselines, SAGA reduces continuous ranked probability score by 31.9 percent at the ten-year horizon and mean absolute error by 37.7 percent at the twenty-year horizon. Conformal intervals achieve nominal coverage to within 0.4 percentage points marginally and within 2.4 percentage points on the worst-case demographic subgroup. The reconstructed lifetime earnings Gini coefficient is 0.327 against the partially observed truth of 0.341 and the GKOS estimate of 0.378. Model weights, calibration tables, and a synthetic equivalent dataset are released for replication outside the protected SCB MONA environment.

In this piece, we reflect on AIES 2025, and outline the conversations and presentations from a discussion session on LLMs in the context of clinical usage and human rights. This is a crosspost from the latest issue of AI Matters, published by the ACM SIAGI. This year's conference on artificial intelligence, ethics and society (AIES) took place in the north of Madrid within the 180m-high tower block that forms the vertical campus of IE University. The event kicked off with a welcome from the chairs and organising committee members, with this opening session also featuring the conference best paper awards. Topics covered during the three-day event included mitigating bias, integrating AI into the workplace, evaluating LLMs in clinical settings, power dynamics in AI ecosystems, and dataset creation.

'Our characters are cybernetic or larger than life,' said Zak London, the founder of Gossip Goblin. 'We adapt to the limits of AI acting.' 'Our characters are cybernetic or larger than life,' said Zak London, the founder of Gossip Goblin. 'We adapt to the limits of AI acting.' 'There are no rules': spotlight on Gossip Goblin as AI film-making enters new era Defying criticisms of'slop' and'theft', the growing culture of AI-powered creativity is attracting interest from Hollywood In a former hemstitching workshop where artisans sewed pleats for Stockholm's 19th-century bourgeoisie, a distinctly 21st-century craft is taking root: AI film-making. One day last week, an actor, director and composer squeezed into a tiny studio booth to record a voiceover for their next AI release. But this had a distinctly homespun feel, the little team fussing over a monologue by a poetic Scottish gorilla inhabiting a transhumanist cyberpunk universe.

Traditional multi-population models, such as the Li-Lee framework, rely on the assumption of mean-reverting country-specific deviations. However, recent data from high-longevity clusters suggest a systemic break in this paradigm. We identify a stationarity paradox where mortality residuals in countries like Sweden and West Germany exhibit persistent unit roots, leading to a systematic mispricing of longevity risk in linear models. To address these non-linearities, we propose Hybrid-Lift, a neural-actuarial framework that combines Hierarchical LSTM networks with a Mean-Bias Correction (MBC) anchoring mechanism. Positioned as a governance-friendly model challenger rather than a replacement of classical approaches, the framework exhibits selective superiority on out-of-sample validation (2012-2020): it outperforms Li-Lee by 17.40% in Sweden and 12.57% in West Germany, while remaining comparable for near-linear regimes such as Switzerland and Japan. We complement the predictive model with an integrated governance suite comprising SHAP-based cross-country influence mapping, a dual uncertainty framework for regulatory capital calibration (Swiss ES 99.0% of +1.153 years), and a reverse stress test identifying the critical shock threshold for solvency buffer exhaustion. This research provides evidence that neural networks, when properly anchored by actuarial principles, can serve as effective model challengers for longevity risk management under the SST and Solvency II standards.

Stabilized regression aims to identify a set of predictors whose conditional relationship with a response variable remains invariant across different environments. Existing graphical characterizations of the stable blanket are mainly developed for structural causal models (SCMs) without hidden variables or causal cycles. However, latent variables and feedback relationships naturally arise in many applications, and they can change both the Markov blanket and the set of predictors that remain stable under interventions. This paper studies stable blankets in graphical causal models with hidden variables, causal cycles, and both features simultaneously. For models with hidden variables, we use acyclic directed mixed graphs (ADMGs) and $m$-separation to characterize the Markov blanket and to construct intervention-stable predictor sets. We introduce the notion of an intervened sub-district and use it to describe how interventions may affect districts connected to the response. For models with cycles, we work with directed graphs (DGs) and directed mixed graphs (DMGs) together with $σ$-separation, treating strongly connected components (SCCs) as the basic graphical units. We then combine these ideas to analyze models with both hidden variables and cycles. The main results give graphical characterizations of Markov blankets, stable frontiers, and stable blankets in these generalized settings. In particular, we identify conditions under which the response is conditionally independent of intervention variables given a suitable predictor set, and we describe when such sets are minimal or unique. These results extend the graphical interpretation of stabilized regression beyond acyclic fully observed models.

In this paper, we study norm-based regularization methods for neural networks. We compare existing penalization approaches and introduce two regularization strategies that extend classical ridge- and lasso-type penalties to neural network models. The first strategy modifies weight decay by incorporating the covariance structure of the input features into a ridge-type $\ell_2$ penalty, allowing regularization to account for feature dependence. The second combines an $\ell_1$ sparsity penalty with covariance-aware $\ell_2$ regularization, producing neural network weights that are both sparse and structurally informed. Monte Carlo simulations are used to evaluate these methods under different data-generating settings, followed by two real-data applications on building cooling-load prediction and leukemia cell-type classification from high-dimensional gene expression data. Across simulated and real-data examples, the proposed regularizers improve predictive performance on unseen data and provide more effective complexity control than standard norm-based penalties, particularly when features are correlated or high-dimensional.

We study the problem of regret minimization in Multi-Agent Multi-Armed Bandits (MAMABs) where the rewards are defined through a factor graph. We derive an instance-specific regret lower bound and characterize the minimal expected number of times each global action should be explored. This bound and the corresponding optimal exploration process are obtained by solving a combinatorial optimization problem whose set of variables and constraints exponentially grow with the number of agents, and cannot be exploited in the design of efficient algorithms. Inspired by Mean Field approximation techniques used in graphical models, we provide simple upper bounds of the regret lower bound. The corresponding optimization problems have a reduced number of variables and constraints. By tuning the latter, we may explore the trade-off between the achievable regret and the complexity of computing the corresponding exploration process. We devise Efficient Sampling for MAMAB (ESM), an algorithm whose regret asymptotically matches the approximated lower bounds. The regret and computational complexity of ESM are assessed numerically, using both synthetic and real-world experiments in radio communications networks.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' The world's militaries spent $2.88 trillion in 2025, an increase of 2.9 percent from the year before, according to the Stockholm International Peace Research Institute's (SIPRI) latest report. To put that number into perspective, $2.88 trillion amounts to $350 of military spending for each person on the planet. In this visual explainer, Al Jazeera unpacks the rise of global militarisation, including how much each nation spends, which countries sell the most weapons, and how military spending compares with spending on healthcare and education. In 2025, the five biggest military spenders were the United States ($954bn), China ($336bn), Russia ($190bn), Germany ($114bn) and India ($92bn), accounting for more than half (58 percent) of world military spending. The US is by far the biggest spender, as it has been every year since World War II.