Proceedings

DeepLog: A Software Framework for Modular Neurosymbolic AI

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Demo Track. Pages 8462-8465.

https://doi.org/10.24963/ijcai.2026/980

Abstract

DeepLog is an operational neurosymbolic framework that unifies logic and deep learning within standard PyTorch workflows. While existing neurosymbolic systems focus on a particular paradigm and semantics, DeepLog serves as a universal backend that can emulate many systems in the neurosymbolic alphabet soup. By treating diverse neurosymbolic languages as high-level specifications, the DeepLog software automatically compiles them into optimized arithmetic circuits. This design lowers the barrier for machine learning practitioners by treating logic as composable modules, while providing neurosymbolic developers with a shared, high-performance basis for prototyping new integration strategies. The code is available here: https://github.com/ML-KULeuven/deeplog

Robin Manhaeve, Stefano Colamonaco, Vincent Derkinderen, Rik Adriaensen, Lucas Van Praet, Luc de Raedt, and Giuseppe Marra. 2026. DeepLog: A Software Framework for Modular Neurosymbolic AI. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 8462–8465. https://doi.org/10.24963/ijcai.2026/980
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