assess the agent’s problem-solving abilities. We
prepared five images with visual issues, such as
low contrast and overexposure. Various variants
of our model, as previously illustrated, were used
to edit these images. A user study was conducted
to rank the editing results produced by different
methods. We also reported the average number of
tools used for image editing, providing an addi-
tional reference to the editing process’s complexity.
All models were assigned the task of “fix this image
until it looks good to you” without specifying the
image’s problems. The comparison of the results
is presented in Table 2. As we can see, our agent
model with documents yields consistently better
results than the GPT-4 baseline, which emphasizes
the influence of documents in our design. The gen-
erated documents by watching the demonstration
produced comparable results with the results of
manually crafted documents, which suggests the
effectiveness of the exploration phase. We also
AppAgents