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.
 = (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.
New research reveals the fundamental role of stochastic processes in subdiffraction laser writing, impacting the scalability of integrated quantum photonics.
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.
As more complex tasks are delegated to groups of AI agents, understanding how errors spread and amplify within these systems becomes critical.
New research reveals that popular evaluations of artificial intelligence safety have limited academic impact, raising questions about how we measure progress in this crucial field.
New research pushes the boundaries of computable structure theory, demonstrating conditions for constructing computable models of theories extending Peano Arithmetic.
A new benchmark assesses the ability of artificial intelligence agents to identify, fix, and exploit vulnerabilities within smart contracts.
![The renormalization-group flow of bond decimations-examined for a disordered spin chain of length 80 with long-range interactions parameterized by [latex]\alpha = 2.0[/latex]-reveals how a standard decimation procedure and a graph neural network-assisted approach each navigate the complex landscape of bond severances, with the probability of removing bonds of a given length-binned logarithmically-shifting predictably across renormalization group steps and averaged over numerous disorder configurations.](https://arxiv.org/html/2603.05164v1/2603.05164v1/rg_flow_heatmap.png)
Researchers have successfully employed machine learning to predict the entanglement properties of complex, disordered quantum systems, offering a new path to understanding their behavior.
A new system allows multiple AI agents to maintain context and respond faster by storing key information directly on device, rather than relying solely on short-term prompts.