A new approach to seismic data reconstruction called SeisEvo uses a multi-agent search driven by large language models (LLMs) to evolve algorithms tha...
Researchers from Ebtesam Al-Haque and Brittany Johnson propose a framework to measure task difficulty in software issue resolution tasks. They analyze...
Researchers propose a new framework for debiased inference using multiple imperfect AI-generated data measurements without relying on gold-standard la...
Researchers have developed a benchmark called FairGlucose to evaluate the fairness of continuous glucose monitoring (CGM) systems. They created a data...
Researchers have developed a new approach to handling missing data in healthcare federated learning called FedCoRe. This method, known as Federated Cr...
Researchers from various institutions have proposed a new method for improving the performance of vision-language models (VLMs) in adapting to real-wo...
Researchers have developed a low-power acoustic anomaly detection system for persistent machine monitoring using an Intel Loihi 2 neuromorphic process...
Researchers have developed a new AI model called MEHnet-MG that can predict molecular properties with high accuracy using just one inexpensive calcula...
Researchers have developed a method to teach AI models to use the minimum amount of authority required to complete a task. This is done by auditing ea...
Researchers have identified a problem in agentic AI systems that can lead to unintended consequences. These systems use multiple pre-action controls, ...
Researchers have proposed a new approach to test-time reasoning in AI systems. The method, called 'candidate-free control,' eliminates the need for se...
Researchers have developed a new framework called Test-Time Self-Distillation with Fisher-Anchored Restoration (TTSD-FAR) to improve the performance o...