Predicting Wind Turbine Health with AI
A new forecasting model leverages transformer networks and multimodal data to anticipate structural responses and improve wind turbine health monitoring.
Science
Safeguarding AI Finance: A Formal Verification Approach
New research details a system for mathematically proving the safety and compliance of autonomous AI agents operating within financial markets.
The Simulated Self: Risks of AI’s Internal Worlds
![Trajectory-persistent adversarial attacks reveal that recurrent state space model (RSSM) architectures amplify initial perturbations-increasing by a factor of 2.26× in the deterministic GRU world model-before GRU contraction attenuates them, a phenomenon not observed in single-step baselines, and which is mitigated through adversarial fine-tuning-reducing amplification across all steps-resulting in a reward gap of only [latex]0.000892 \pm 0.000057[/latex] at a planning horizon of 30, and demonstrating a fundamental trade-off between model expressiveness and robustness to adversarial input.](https://arxiv.org/html/2604.01346v1/x1.png)
As artificial intelligence builds increasingly complex models of reality, new safety vulnerabilities emerge from the systems’ imagined environments.
Beyond Algorithms: Building Self-Managing Investment Portfolios
A new agentic architecture is emerging that moves beyond traditional algorithmic investing, offering a pathway to fully autonomous portfolio management.
Predicting Supply Chain Chaos with the Power of News
A new approach leverages large language models and real-time news analysis to forecast disruptions and improve supply chain resilience.
Untangling Complexity: A New Path to Predicting System Behavior
![The SIGN framework demonstrates successful equation discovery across diverse networked dynamical systems - including Kuramoto phase-oscillator networks, susceptible-infected-susceptible (SIS) epidemic models, Michaelis-Menten regulatory networks, FitzHugh-Nagumo neuron models, and Hindmarsh-Rose neuron models - consistently inferring coefficient values with low error rates across varying network sizes and topologies, from synthetic scale-free networks ([latex]10^3[/latex] and [latex]10^5[/latex] nodes) to large empirical datasets like GitHub, Catster, and a human brain network.](https://arxiv.org/html/2604.00599v1/x2.png)
Researchers have developed a novel framework that combines the power of data and physics-based modeling to forecast the long-term evolution of massive, interconnected systems.
Can AI Know What It Doesn’t Know?
![The study dissects the confidence scaling of large language models-specifically GPT-5, DeepSeek-V3.2-Exp, and Mistral-Medium-2508-across three distinct tasks, revealing disparities in their ability to align reported confidence levels with task accuracy, as evidenced by metrics like [latex]d'\relax[/latex] and [latex]\text{Mrati}\relax[/latex], and further refined by the exclusion of outlier data points-approximately 0.1% for Mistral-Medium-2508 in task B-to ensure a robust assessment of confidence calibration across a trial count of [latex]2 \times \Gamma_{3}0^{\relax}[/latex] for task A and [latex]\Gamma_{3}0^{\relax}[/latex] for tasks B and C.](https://arxiv.org/html/2603.29693v1/x1.png)
New research explores whether large language models possess the capacity for metacognition – the ability to assess their own confidence and uncertainty.
When AI Teams Turn Sour: The Risks of Emergent Behavior
As increasingly complex AI systems begin to collaborate, unforeseen and potentially harmful behaviors can arise from the interactions of individually rational agents.
Catching the Shift: New Warnings for Unpredictable Systems
A new study reveals improved methods for predicting abrupt changes in dynamic systems subjected to slow, repeating forces.
The Shape of Things to Come: Topology and Complex Systems
A growing body of research demonstrates that topological methods offer a powerful new lens for understanding organization and change in complex systems, moving beyond traditional approaches.