{"type":"ScholarlyArticle","title":"DeepLog: A Software Framework for Modular Neurosymbolic AI","authors":[{"name":"Robin Manhaeve","slug":"robin-manhaeve","given":"Robin","family":"Manhaeve","affiliation":""},{"name":"Stefano Colamonaco","slug":"stefano-colamonaco","given":"Stefano","family":"Colamonaco","affiliation":""},{"name":"Vincent Derkinderen","slug":"vincent-derkinderen","given":"Vincent","family":"Derkinderen","affiliation":""},{"name":"Rik Adriaensen","slug":"rik-adriaensen","given":"Rik","family":"Adriaensen","affiliation":""},{"name":"Lucas Van Praet","slug":"lucas-van-praet","given":"Lucas","family":"Van Praet","affiliation":""},{"name":"Luc De Raedt","slug":"luc-de-raedt","given":"Luc","family":"de Raedt","affiliation":""},{"name":"Giuseppe Marra","slug":"giuseppe-marra","given":"Giuseppe","family":"Marra","affiliation":""}],"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","doi":"10.24963/ijcai.2026/980","url":"https://ijcai.org/proceedings/2026/980","pdf":"https://ijcai.org/proceedings/2026/0980.pdf","markdown":"https://ijcai.org/proceedings/2026/980.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/980","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Demo Track","track":"Demo Track","number":980,"pages":{"first":8462,"last":8465},"keywords":["AI: Uncertainty in AI","AI: Machine Learning","AI: Knowledge Representation and Reasoning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}