4 papers · 1 filter
Flowing Through States: Neural ODE Regularization for Reinforcement Learning
Mohamed Ghanem, Bernd Finkbeiner
Neural networks applied to sequential decision-making tasks typically rely on latent representations of environment states. While environment dynamics dictate how semantic states e…
Natural Synthesis: Outperforming Reactive Synthesis Tools with Large Reasoning Models
Frederik Schmitt, Matthias Cosler, Niklas Metzger +4
Reactive synthesis, the problem of automatically constructing a hardware circuit from a logical specification, is a long-standing challenge in formal verification. It is elusive fo…
Learning Representations Through Contrastive Neural Model Checking
Vladimir Krsmanovic, Matthias Cosler, Mohamed Ghanem +1
Model checking is a key technique for verifying safety-critical systems against formal specifications, where recent applications of deep learning have shown promise. However, while…
Learning Better Representations From Less Data For Propositional Satisfiability
Mohamed Ghanem, Frederik Schmitt, Julian Siber +1
Training neural networks on NP-complete problems typically demands very large amounts of training data and often needs to be coupled with computationally expensive symbolic verifie…