AI Model Controls Context Without Growing Memory

Researchers propose an intervention-based architecture that enables AI systems to handle context-dependent decision-making by modifying a shared recurrent state rather than expanding memory capacity. The approach matches performance of larger memory models on context-switching tasks, offering more efficient contextual control for sequential decision-making under uncertainty.