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DEER: A Benchmark for Evaluating Deep Research Agents on Expert Report Generation
📄 Paper: OpenReview 🏆 Accepted at ICML 2026
Existing evaluations for Deep Research Agents often remain surface-level when assessing long-form expert reports. DEER enables evaluation from an expert perspective using 101 fine-grained rubrics and task-specific human expert evaluation guidance. It also links every factual statement in a report to relevant external evidence and verifies whether the content is actually grounded, enabling a more complete assessment of report reliability.
Dataset Access
The complete dataset is distributed as a password-protected 7z archive.
Archive: deer_dataset.7z
Password: 0000
7z x deer_dataset.7z
The archive is password-protected to prevent web-enabled agents from finding and using expert-written evaluation guidance that specifies the key content expected in reports for each DEER query.
Usage
DEER may be used for non-commercial research, evaluation, model training, fine-tuning, continued pretraining, and development.
To preserve the integrity of the benchmark:
- do not redistribute, mirror, republish, or publicly host the original dataset;
- do not upload the extracted dataset to public repositories, websites, dataset platforms, or file-sharing services; and
- direct other users to this official repository instead of sharing copies of the dataset.
Commercial use requires prior written permission from LG AI Research.
See the full DEER Dataset License.
Citation
@inproceedings{
han2026deer,
title={{DEER}: A Benchmark for Evaluating Deep Research Agents on Expert Report Generation},
author={Janghoon Han and Heegyu Kim and Changho Lee and Dahm Lee and Min Hyung Park and Hosung Song and Stanley Jungkyu Choi and Moontae Lee and Honglak Lee},
booktitle={Forty-third International Conference on Machine Learning},
year={2026},
url={https://openreview.net/forum?id=ILRx5neJk6}
}
License
Copyright © 2026 LG AI Research. All rights reserved.
DEER is released under the custom DEER Dataset License.
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