![The accelerating concentration of capital within a handful of technology firms-a modern iteration of feudalism dubbed “technofeudalism”-is not merely an economic shift, but a systemic crisis [latex] \text{the metacrisis} [/latex] fueled by the disproportionate returns to scale inherent in digital infrastructure and artificial intelligence.](https://arxiv.org/html/2512.24863v1/metacrisis.png)
A new analysis argues that the rapid advancement of large AI models is exacerbating interconnected environmental, social, and linguistic crises, demanding a fundamental rethinking of natural language processing.
![A consistent threshold of [latex]\theta=0.06[/latex] applied to both a forward-oriented causal observable [latex]\mathscr{F}(t)[/latex] and the raw composite signal [latex]\mathscr{F}_{0}(t)[/latex] demonstrates comparable performance-as measured by cumulative returns relative to buy-and-hold-and yields a similar frequency of state changes (trades) when applied to one-minute EURUSDT data.](https://arxiv.org/html/2512.24621v1/x3.png)
This review details a method for building causally valid predictive signals from financial time series, offering improved performance in specific market conditions.
A new approach leverages latent space representation and graph neural networks to reliably identify IoT botnets even as attack patterns evolve.
![The simulation environment explores the relationship between residual magnetic flux and the closing angle of a transformer’s core, demonstrating how these inputs directly influence the magnitude of the resulting peak inrush current[10].](https://arxiv.org/html/2512.22190v1/x2.png)
This review explores how artificial intelligence, particularly neural networks, is being used to monitor, diagnose, and optimize the performance of critical power transformer infrastructure.
New research demonstrates how integrating physics-based models with artificial intelligence significantly enhances the accuracy and trustworthiness of transformer health monitoring.
A new framework simulates the dynamics of venture capital investment using interacting AI agents to predict startup success with greater accuracy.
As digital lending scales, maintaining the accuracy of credit risk models requires continuous adaptation and robust monitoring.
New research analyzes over 2,500 crash records to identify key patterns and contributing factors in vehicles with varying levels of automation.
A new approach leverages the power of artificial intelligence to anticipate network traffic and optimize performance for next-generation wireless systems.
![Resilience against attack vectors isn’t a static property, but emerges from a dynamic interplay between vulnerabilities, network state [latex]X_{t}[/latex], and three classes of defense-proactive hardening, responsive adaptation, and retrospective learning from performance outcomes [latex]Y_{t}[/latex]-that continuously refine the system’s ability to withstand compromise.](https://arxiv.org/html/2512.22721v1/Figures/resilienceMechanism.png)
This review explores the evolving threat landscape and emerging strategies for building cyber-resilient next-generation networks.