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cs.LG2025
Iterative In-Context Learning to Enhance LLMs Abstract Reasoning: The Case-Study of Algebraic Tasks
Stefano Fioravanti, Matteo Zavatteri, Roberto Confalonieri +4
LLMs face significant challenges in systematic generalization, particularly when dealing with reasoning tasks requiring compositional rules and handling out-of-distribution example…
cs.LG2025
Discovering Generalizable Governing Equations for Graph Dynamical Systems with Interpretable Neural Networks
Riccardo Cappi, Paolo Frazzetto, Nicolò Navarin +1
The discovery of symbolic governing equations is a central goal in science; yet, it remains challenging particularly for graph dynamical systems, where the network topology further…
cs.LG2025
Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks
Maximilian Muschalik, Fabian Fumagalli, Paolo Frazzetto +5
Albeit the ubiquitous use of Graph Neural Networks (GNNs) in machine learning (ML) prediction tasks involving graph-structured data, their interpretability remains challenging. In…