  Spreadsheet manipulation is widely existing in most daily works and
significantly improves working efficiency. Large language model (LLM) has been
recently attempted for automatic spreadsheet manipulation but has not yet been
investigated in complicated and realistic tasks where reasoning challenges
exist (e.g., long horizon manipulation with multi-step reasoning and ambiguous
requirements). To bridge the gap with the real-world requirements, we introduce
$\textbf{SheetRM}$, a benchmark featuring long-horizon and multi-category tasks
with reasoning-dependent manipulation caused by real-life challenges. To
mitigate the above challenges, we further propose $\textbf{SheetAgent}$, a
novel autonomous agent that utilizes the power of LLMs. SheetAgent consists of
three collaborative modules: $\textit{Planner}$, $\textit{Informer}$, and
$\textit{Retriever}$, achieving both advanced reasoning and accurate
manipulation over spreadsheets without human interaction through iterative task
reasoning and reflection. Extensive experiments demonstrate that SheetAgent
delivers 20-30% pass rate improvements on multiple benchmarks over baselines,
achieving enhanced precision in spreadsheet manipulation and demonstrating
superior table reasoning abilities. More details and visualizations are
available at https://sheetagent.github.io.
