  The attainment of autonomous operations in mobile computing devices has
consistently been a goal of human pursuit. With the development of Large
Language Models (LLMs) and Visual Language Models (VLMs), this aspiration is
progressively turning into reality. While contemporary research has explored
automation of simple tasks on mobile devices via VLMs, there remains
significant room for improvement in handling complex tasks and reducing high
reasoning costs. In this paper, we introduce MobileExperts, which for the first
time introduces tool formulation and multi-agent collaboration to address the
aforementioned challenges. More specifically, MobileExperts dynamically
assembles teams based on the alignment of agent portraits with the human
requirements. Following this, each agent embarks on an independent exploration
phase, formulating its tools to evolve into an expert. Lastly, we develop a
dual-layer planning mechanism to establish coordinate collaboration among
experts. To validate our effectiveness, we design a new benchmark of
hierarchical intelligence levels, offering insights into algorithm's capability
to address tasks across a spectrum of complexity. Experimental results
demonstrate that MobileExperts performs better on all intelligence levels and
achieves ~ 22% reduction in reasoning costs, thus verifying the superiority of
our design.
