A new wave of research is transforming uncertainty quantification from a diagnostic tool into a powerful control signal for large language models, improving reasoning and enabling more robust AI agents.
New research demonstrates how explainable AI techniques can improve the performance and trustworthiness of machine learning models used in critical industrial applications.
A large-scale international evaluation reveals significant vulnerabilities in AI agents tasked with complex actions, highlighting critical gaps in safety methodologies.
New research reveals that commonly used financial models underestimate cryptocurrency risk, potentially leaving investors unprepared for significant losses.
![Sectoral distinctions in company risk profiles emerge organically from the data, as demonstrated by the substantial divergence in similarity distributions-companies sharing two-digit Standard Industrial Classification codes exhibit markedly higher risk profile similarity [latex] (5,263 pairs) [/latex] than those in different industries [latex] (101,228 pairs) [/latex], despite the taxonomy mapping process being explicitly devoid of industry-specific information.](https://arxiv.org/html/2601.15247v1/x1.png)
A new approach uses artificial intelligence to automatically identify and categorize potential risks disclosed in company filings.
As cloud infrastructure grows, so does the deluge of alerts, demanding intelligent systems to prioritize critical issues and reduce operator burnout.
New research shows that structuring AI systems like human organizations – with specialized roles and independent checks – dramatically improves their performance and error detection.
![UAV swarm architectures are explored through three distinct deployments-standalone, edge-enabled, and edge/cloud-enabled-each offering varying levels of computational resource allocation to individual agents and collectively enabling a range of applications dependent on optimized agent behavior as defined by [latex] agent_{i} [/latex].](https://arxiv.org/html/2601.14437v1/x1.png)
A new approach combining agentic artificial intelligence and edge computing is enabling more scalable and resilient autonomous operation for drone swarms.
As demand from artificial intelligence data centers surges, a novel optimization framework aims to bolster grid resilience against unpredictable load fluctuations.
As large language models become increasingly powerful, ensuring their responsible development and deployment is paramount.