arXiv cs.CLPaper
The Emergent Symbolic Structure of Artificial Neural Networks
This is a solid interpretability contribution that bridges the neural-symbolic divide. If reproducible across architectures, it changes how we think about what happens inside models: you don't have to choose between symbolic reasoning and neural learning, they might be the same thing. For practitioners building interpretable systems, this opens a path to extracting structured representations from trained models without throwing away the neural computation.