A new dynamic bootstrapping method improves the accuracy and efficiency of training machine learning interatomic potentials by actively filtering out noisy data during the learning process.
Researchers have developed a new AI system that combines visual data with language processing to better identify and understand anomalies in complex industrial environments.
Researchers have developed a new approach to time-series anomaly detection that combines the reasoning power of large language models with the rigor of classical statistical analysis.
New research explores how artificial intelligence can refine the analysis of prediction markets by identifying truly meaningful relationships between events.
New research explores whether artificial intelligence agents exhibit predictable behavioral patterns in financial markets, specifically when shifting between different investment strategies.
As vehicles become more autonomous, drivers develop complex mental models to interpret the actions of opaque algorithms, but lack the tools to influence their behavior.
![The study demonstrates substantial variation in the zero-shot performance of twenty-three object detectors across twelve datasets, revealing both dataset-dependent limitations-where detector efficacy fluctuates considerably-and inherent quality stratification among the detectors themselves, ranging from those performing significantly below chance (red) to those achieving high accuracy (green) as measured against a [latex] 50\% [/latex] random guess baseline.](https://arxiv.org/html/2602.07814v1/x1.png)
A new study reveals that out-of-the-box AI-generated image detectors struggle with real-world performance and exhibit surprising inconsistencies.
As deepfake technology rapidly evolves, current detection methods struggle to keep pace, creating a significant and growing vulnerability.
A new framework harnesses the power of artificial intelligence to dynamically discover and refine investment strategies, adapting to the ever-changing conditions of modern markets.
A new benchmark challenges large language models with graph algorithm problems, exposing limitations in their ability to handle complex relational data and revealing a tendency to overthink.