tl;dr: Researchers show that neural networks' continuous vector representations can be closely approximated by closed-form symbolic structures, suggesting networks implicitly implement symbolic computation. They demonstrate this across small list-manipulation networks and LLMs operating in arithmetic, logic, code, and language, and show that targeted interventions on these identified symbolic structures reliably alter LLM behavior. The finding offers a bridge between classical symbolic theories of intelligence and modern vector-based AI.