Drone Hacking: 5G’s Hidden Weakness
New research reveals critical vulnerabilities in 5G Standalone networks that could allow attackers to compromise unmanned aerial vehicle (UAV) communications and control.
Science
Chasing Ghosts in the Machine: Securing Collaborative AI Systems
As artificial intelligence increasingly relies on interconnected agents, detecting sophisticated attacks requires tracing the flow of information between them.
The Topology of Trust: Solving Elections in Anonymous Networks
![A quasi-covering, specifically the ball [latex]B_{\mathbf{D}_1}(v_1, r)[/latex], identifies regions within one graph ([latex]\mathbf{D}_1[/latex]) that exhibit local structural similarity to another graph ([latex]\mathbf{D}_0[/latex]), effectively demonstrating the existence of substantial areas sharing a common local appearance.](https://arxiv.org/html/2603.05118v1/2603.05118v1/x1.png)
New research reveals how understanding network structure can unlock reliable leader election even when identities are hidden and randomness is shared.
Beyond the Horizon: When Black Holes Lose Their Identity
![The study delineates the domain of existence for solutions induced by curvature, quantifying deviations from the Kerr solution-characterized by dimensionless spin and coupling parameter [latex] -\alpha/M^{2} [/latex]-through measurable properties including horizon area [latex] A_{s} [/latex], Hawking temperature [latex] T_{s} [/latex], entropy [latex] S_{s} [/latex], and scalar charge [latex] Q_{s} [/latex].](https://arxiv.org/html/2603.05064v1/2603.05064v1/charge.png)
New research demonstrates that black holes aren’t always defined by just mass and spin, revealing multiple stable configurations arising from a complex interplay of gravity and scalar fields.
Counting to Complexity: Analyzing Reachability in Counter Automata
A new analysis defines the limits of determining reachable states in vector addition systems with integer counters, offering insights into the inherent computational difficulty of these systems.
Smarter Federated Learning: Balancing Security, Accuracy, and Speed
A new approach interleaves privacy-enhancing techniques to optimize the delicate trade-off between data protection, model quality, and computational cost in distributed machine learning.
Unwinding Quantum Change: How Symmetry Reveals Dynamical Transitions
 = (0.5, -1)[/latex] to either [latex](\mu, \Delta) = (1.5, 1)[/latex] or [latex](0.8, 1)[/latex] - exhibit distinct behaviors dependent on the number of modes satisfying the condition [latex]\cos(2\delta\theta_{k_c}) = 0[/latex]; specifically, the resulting dynamical mode energy [latex]\tilde{\lambda}_{k_n}(t)[/latex] at time [latex]t_c[/latex] varies with the parameter [latex]m_2[/latex], highlighting a nuanced relationship between quench protocols, mode excitation, and system energy.](https://arxiv.org/html/2603.05284v1/2603.05284v1/x2.png)
New research clarifies the behavior of quantum systems undergoing rapid change by tracking how their internal symmetries evolve over time.
The Random Walk of Quantum Light
New research reveals the fundamental role of stochastic processes in subdiffraction laser writing, impacting the scalability of integrated quantum photonics.
Squeezing More From Every Bit: A New Approach to Model Compression
Researchers have developed a novel quantization method that dramatically reduces model size with minimal loss of accuracy, pushing the boundaries of efficient large language model deployment.
When Collaboration Goes Wrong: The Hidden Risks of AI Teams
As more complex tasks are delegated to groups of AI agents, understanding how errors spread and amplify within these systems becomes critical.