DeepLog: A Software Framework for Modular Neurosymbolic AI

2026-05-11

AI Summary

Existing neuro-symbolic systems are often confined to specific paradigms, making it difficult to unify logical reasoning with deep learning and resulting in high barriers to entry. This work proposes a general-purpose neuro-symbolic AI backend framework that, for the first time, enables unified compilation and modular composition of multiple neuro-symbolic languages. The framework automatically compiles high-level logical specifications into optimized arithmetic circuits and seamlessly integrates them into PyTorch workflows. By treating logic as composable components and leveraging automatic differentiation alongside modular encapsulation, the approach significantly lowers the usability barrier for practitioners while offering researchers an efficient platform for rapid prototyping. The implementation is publicly available as open-source code.

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

Problem

• neurosymbolic AI
• logic integration
• deep learning
• software framework
• arithmetic circuits

Innovation

• neurosymbolic AI
• modular framework
• arithmetic circuits
• logic compilation
• PyTorch integration