Language models achieve task flexibility not by swapping components, but by dynamically rewiring how shared components respond to task context and influence each other—a finding that could improve model interpretability and enable safer targeted interventions.
This paper reveals how language models reuse the same components across different tasks by dynamically changing which information they process and how they influence downstream layers.