Datasets:
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metadata
license: cc-by-nc-4.0
language:
- en
- zh
task_categories:
- text-generation
tags:
- agents
- tool-use
- office-automation
- long-horizon
pretty_name: OfficeInstruct
size_categories:
- 100K<n<1M
extra_gated_prompt: >-
OfficeInstruct is released for non-commercial research only, under the CC
BY-NC 4.0 license. The data were contributed by invited participants and
de-identified before release. Access requests are reviewed manually. Please
describe your intended use below.
extra_gated_fields:
Full name: text
Affiliation (university or organization): text
Country: country
Position:
type: select
options:
- Student
- Researcher / Faculty
- Engineer
- Other
Intended use (please describe briefly): text
I will use the dataset for non-commercial research only: checkbox
I will not attempt to re-identify any individual in the dataset: checkbox
I will cite the OfficeInstruct paper in any work that uses the dataset: checkbox
extra_gated_button_content: Submit access request
OfficeInstruct
OfficeInstruct is a dataset of long-trajectory, multi-turn interactions of tool-augmented office agents across documents (Doc), spreadsheets (Sheet), and presentations (PPT). Trajectories were collected with the OfficeAgent framework and filtered and repaired with a graph-based evidence evaluation that removes redundant tool cycles, execution errors, and unsupported generations.
Access
This dataset is gated. Please fill in the request form above; requests are reviewed manually. By requesting access you agree to use the data for non-commercial research only, under the CC BY-NC 4.0 license.
Structure
train-data/: training trajectories for Doc, Sheet, and PPT tasks (JSONL).eval-data/: evaluation data (JSONL), together with copies of public benchmarks used in the paper (TableBench, SpreadsheetBench, TReB, MMTU). These benchmarks are redistributed for convenience only and remain subject to their original licenses.
Citation
If you use OfficeInstruct, please cite:
@inproceedings{xu2026officeinstruct,
title = {OfficeInstruct: A Dataset for Tool-Augmented Office Agents on Long-Trajectory Generative Tasks},
author = {Xu, Weikai and Jiang, Zhizheng and Jiang, Lushuo and Huang, Kun and Liu, Yuxuan and Gao, Pengzhi and Liu, Wei and Luan, Jian and Hu, Xiaolin and An, Bo},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track},
year = {2026}
}