将逻辑与深度学习融合，让开发者像搭积木一样构建神经符号系统。
DeepLog: A Software Framework for Modular Neurosymbolic AI
- 把不同神经符号语言当作高级规格，自动编译成高效算术电路。
- 兼容多种神经符号系统，可作为统一开发和部署的高性能后端。
- 适合想快速实验新融合方法的研究者和工程实践者。
DeepLog 是一个可操作的神经符号框架，将逻辑与深度学习统一于标准 PyTorch 工作流中。现有神经符号系统多聚焦特定范式与语义，而 DeepLog 作为通用后端，能模拟神经符号领域中的多种系统。通过将多样化的神经符号语言视为高层规格，DeepLog 自动将其编译为优化的算术电路。该设计降低了机器学习从业者使用逻辑模块的门槛，同时为神经符号开发者提供共享、高性能的原型开发基础。代码已公开：https://github.com/ML-KULeuven/deeplog。
原文摘要 · Abstract (English)
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
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