The Emergent Symbolic Structure of Artificial Neural Networks

The authors demonstrate that internal vector representations of diverse neural networks can be closely approximated by explicit symbolic structures, allowing the entire representation-generating process to be replaced with a closed-form equation while preserving behavior. This holds for small networks manipulating lists and for large language models across arithmetic, logic, code, and language tasks, and enables targeted behavioral modifications through precise interventions on the identified symbolic structures.