Entity Tracking

Do Language Models Track Entities Across State Changes?

This paper investigates the mechanistic basis of entity tracking (ET) in transformer language models under realistic, multi-operation scenarios involving state changes (PUT, REMOVE, MOVE). The authors find that LMs do not incrementally update world states but instead aggregate relevant information in parallel at the final token once a query is apparent. A key finding is that the REMOVE operation is implemented via a fragile global suppression tag, which predicts specific failure modes confirmed behaviorally. The authors propose a mechanistic fix—nullifying this tag—and argue that behavioral and mechanistic analyses can productively inform each other.