Towards Robust Real-World Spreadsheet Understanding with Multi-Agent Multi-Format Reasoning

Introduction

We present Spreadsheet Agent, a two-stage multi-agent framework for robust spreadsheet understanding. By incrementally reading local spreadsheet regions with multimodal signals and verifying extracted structures before reasoning, Spreadsheet Agent handles large real-world spreadsheets more effectively and surpasses the ChatGPT Agent on Spreadsheet Bench.

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Data

All data are packaged in data.tar.xz. You can decompress it directly in the root directory of the project.

tar -xvf data.tar.xz -C ./

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Pre Setup

Jupyter Server

All code is located in the code_exec_docker folder. You can start the Jupyter server as follows:

# Step 1: Download the docker image
docker pull docker.io/xingyaoww/codeact-executor
# Step 2: Start the Jupyter server
bash start_jupyter_server.sh

Excel to Image

The Excel-to-Image feature requires the win32com package, which is only available on Windows.

python core/excel2image.py

Extract Structure Information

Both Qwen3-Coder-480B-A35B and GLM-4.5V need to be deployed with vLLM.

python extractor.py \
--extractor yaml_desc_verify \
--dataset spreadsheet \
--url url_of_qwen3_coder \
--vision_url url_of_glm_4.5v \
--suffix 480b_glm45v

Then you can find the extracted structure information in data/spreadsheet.

Evaluation

After extraction, you can run the evaluation on SpreadsheetBench:

python spreadsheet.py --url url_of_qwen3_coder --suffix 480b_glm45v