Building an AI agent that can carry out user tasks often means sending private data to a large cloud-hosted model. These models are the most accurate, but they require access to the user's personal information. Small models that run on edge devices can keep that data local, but they typically lack the capability to solve complex tasks on their own.
This paper examines a hybrid arrangement: a large cloud model writes the agent program, and a small local model interprets and executes it on the device. The idea is to combine the accuracy of the cloud model with the privacy of local processing. The study focuses on whether small language models are up to the job of interpreting these cloud-written programs.
The provided abstract is truncated before presenting results, so this article can only describe the research question rather than its findings. The trade-off between accuracy and privacy remains an open problem.