![The study dissects the interplay between trust and robustness within AI-driven cybersecurity systems, revealing that heightened reliance on artificial intelligence does not automatically equate to increased resilience against evolving threats - a paradox explored through research question three [latex] RQ_3 [/latex].](https://arxiv.org/html/2603.23304v1/x9.png)
New research reveals that despite the potential of artificial intelligence to transform cyber threat intelligence, practical barriers are slowing its adoption in the financial sector.
Researchers introduce a new dataset and benchmark to evaluate how well artificial intelligence can translate daily financial news into effective investment strategies for the Chinese market.
New research shows that analyzing public conversations on Reddit with streamlined AI models can reveal surprisingly accurate insights into inflation expectations and even predict key economic indicators.
New research reveals that AI weather models can surprisingly lose their ability to accurately forecast tropical cyclone intensity under specific atmospheric conditions, raising concerns about the reliability of these systems.
![The study demonstrates how network topology fundamentally shapes the propagation of trust and perception of events, with differing structures - random networks, echo chambers, and star-shaped influencer models - yielding distinct trust trajectories and perceived event intensities, as quantified by the spectral radius [latex]\rho(J_{2n})[/latex] of the influence matrix.](https://arxiv.org/html/2603.20248v1/x6.png)
New research reveals how quickly public confidence in AI governance can erode, leading to potentially destabilizing social consequences.
A new data-driven system harnesses the power of machine learning to forecast global ocean conditions with impressive accuracy and efficiency.
A new method quantifies how interconnected facts are stored within large language models, enabling more accurate and efficient editing without unintended consequences.
Researchers have developed a novel framework to enhance the accuracy and understanding of artificial intelligence models predicting high-impact weather events.
New research combines artificial intelligence with human factors analysis to better understand and predict how operators perceive critical information in nuclear power plants.
Researchers are leveraging the principles of macroscopic physical laws and advanced deep learning to more accurately forecast the spread of information online.