  With the advancement of Large-Language Models (LLMs) and Large
Vision-Language Models (LVMs), agents have shown significant capabilities in
various tasks, such as data analysis, gaming, or code generation. Recently,
there has been a surge in research on web agents, capable of performing tasks
within the web environment. However, the web poses unforeseeable scenarios,
challenging the generalizability of these agents. This study investigates the
disparities between human and web agents' performance in web tasks (e.g.,
information search) by concentrating on planning, action, and reflection
aspects during task execution. We conducted a web task study with a think-aloud
protocol, revealing distinct cognitive actions and operations on websites
employed by humans. Comparative examination of existing agent structures and
human behavior with thought processes highlighted differences in knowledge
updating and ambiguity handling when performing the task. Humans demonstrated a
propensity for exploring and modifying plans based on additional information
and investigating reasons for failure. These findings offer insights into
designing planning, reflection, and information discovery modules for web
agents and designing the capturing method for implicit human knowledge in a web
task.
