Multi-device agents need hierarchical recovery strategies that distinguish between local device failures (fixable with alternative approaches) and global failures (requiring task replanning), rather than treating all failures the same way.
This paper presents H-RePlan, a framework that helps AI agents recover from failures when working across multiple devices (like computers and phones). Instead of replanning entire tasks when something goes wrong, the system first tries to fix problems locally on each device, only escalating to global replanning when necessary.