3 papers
cs.AI2026
Explaining AI Agents Through Execution Traces
Vittoria Vineis, Fabiano Veglianti, Lorenzo Antonelli +3
AI Agents are increasingly deployed in real-world settings, where they interact with external tools and make sequential decisions with limited human oversight. This creates a press…
cs.CL2026
PONTE: Personalized Orchestration for Natural Language Trustworthy Explanations
Vittoria Vineis, Matteo Silvestri, Lorenzo Antonelli +2
Explainable Artificial Intelligence (XAI) seeks to enhance the transparency and accountability of machine learning systems, yet most methods follow a one-size-fits-all paradigm tha…
cs.LG2026
Counterfactual Explanations for Hypergraph Neural Networks
Fabiano Veglianti, Lorenzo Antonelli, Gabriele Tolomei
Hypergraph neural networks (HGNNs) effectively model higher-order interactions in many real-world systems but remain difficult to interpret, limiting their deployment in high-stake…