How can self-evolving agents automate cross-app tasks?
MobileSteward: Integrating Multiple App-Oriented Agents with Self-Evolution to Automate Cross-App Instructions
This paper introduces MobileSteward, a multi-agent framework designed to automate complex tasks spanning multiple mobile apps using LLMs. It addresses challenges like intricate task relationships, diverse app environments, and error propagation in multi-step instructions. Key LLM-related points include: using app-specialized LLMs (StaffAgents) coordinated by a central LLM (StewardAgent), dynamic task allocation based on predicted information flow, LLM-driven action prediction within each app, reflection and adjustment by the StewardAgent based on execution history, and memory modules in both agent types to learn from successful executions, enabling continuous improvement of the system's performance. The researchers also introduce CAPBench, a new benchmark for evaluating cross-app instruction automation.