NeSyCat: Monad-Based Semantics for Neurosymbolic AI
Discover NeSyCat, a monad-based framework unifying semantics in the ULLER neurosymbolic AI system for enhanced modularity and reasoning.
Evaluating Vision-Language Models for Astronomy Tasks
Discover how vision-language models perform in observational astronomy across imaging, spectroscopy, and photometry with the AstroVLBench benchmark.
FastOMOP: Automated Real-World Evidence on OMOP CDM Data
Discover FastOMOP, a reliable multi-agent system automating real-world evidence generation on OMOP CDM data with safety and governance features.
Scenario-Aware Legal Compliance for Autonomous Driving
Discover how AI and scenario-based methods improve autonomous vehicles' compliance with traffic laws for safer, lawful driving.
Hierarchical Behaviour Spaces in Reinforcement Learning
Explore Hierarchical Behaviour Spaces, a novel method enhancing exploration and adaptability in reinforcement learning with multiple reward functions.