Researchers have conducted a comparative study of out-of-the-box technology for automatic target detection and recognition (ATD/R). They benchmarked s...
Researchers analyzed how students interact with AI systems while working on programming tasks. They used a taxonomy to classify 830 student inquiries ...
Researchers have proposed a new framework for solving the Lifelong Multi-Agent Path Finding problem, which involves multiple agents navigating through...
Researchers have proposed a formal model called Collective Counterfactual Planning (CCP) to describe how groups of agents can work together on project...
Researchers have found that small language models can grade open-ended examination answers as accurately as more expensive models when they use an exp...
Researchers have proposed a new method for scheduling reinforcement learning with verifiable rewards (RLVR) tasks. The approach uses a graph-based onl...
Researchers have developed a new framework called SIGMA that uses SHAP values to guide the generation of features for Large Language Models (LLMs) wit...
Researchers have identified a potential issue in continuous code world models, which are used to generate executable world models for classical planne...
Researchers have found a problem with some AI-powered systems used to detect anomalies in computer logs. These systems often give too much confidence ...
Researchers have developed a method to improve the accuracy of ultrasound tongue segmentation under extreme data scarcity. The approach, called Dual C...
Researchers have developed a framework called TSN4PI to track how people's political beliefs change over time on social media. The system uses large l...
Researchers propose a framework called Traceable Trust to ensure the reliability of artificial intelligence in bioscience. They argue that AI outputs ...