DeepLog: A Software Framework for Modular Neurosymbolic AI
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917480005369856 |
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| author | Manhaeve, Robin Colamonaco, Stefano Derkinderen, Vincent Adriaensen, Rik Van Praet, Lucas De Raedt, Luc Marra, Giuseppe |
| author_facet | Manhaeve, Robin Colamonaco, Stefano Derkinderen, Vincent Adriaensen, Rik Van Praet, Lucas De Raedt, Luc Marra, Giuseppe |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_10279 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DeepLog: A Software Framework for Modular Neurosymbolic AI Manhaeve, Robin Colamonaco, Stefano Derkinderen, Vincent Adriaensen, Rik Van Praet, Lucas De Raedt, Luc Marra, Giuseppe Machine Learning 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 |
| title | DeepLog: A Software Framework for Modular Neurosymbolic AI |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.10279 |