DR³-Eval: Towards Realistic and Reproducible
Deep Research Evaluation

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✨ Overview

DR³-Eval is a realistic, reproducible, and multimodal evaluation benchmark for Deep Research Agents, focusing on multi-file report generation tasks.

Existing benchmarks face a fundamental tension between realism, controllability, and reproducibility when evaluating deep research agents. DR³-Eval addresses this through the following design:

• 🔬 Real User Scenarios: Tasks are constructed from real user-provided multimodal files, covering 3 major domains and 13 sub-domains
• 📦 Static Sandbox Corpora: An independent static research sandbox is built for each task, containing supportive, distracting, and noisy documents
• 🎯 Reverse Construction Method: Queries are reverse-engineered from verified evidence documents, eliminating evaluation ambiguity
• 📊 Multi-dimensional Evaluation: Five dimensions — Information Recall, Factual Accuracy, Citation Coverage, Instruction Following, and Depth Quality

Figure 1. Comparison of DR³-Eval with existing deep research benchmarks. DR³-Eval supports both user files and sandbox corpora, providing a realistic and reproducible multimodal evaluation environment.

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🏆 Benchmark Comparison

DR³-Eval is the first deep research evaluation benchmark that simultaneously satisfies all of the following: user file input, static sandbox corpora, multimodality, real-world scenarios, multi-file upload, and reverse construction.

Figure 2. Comprehensive comparison of DR³-Eval with representative benchmarks.

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🧩 Framework and Pipeline

The overall framework of DR³-Eval consists of three core components:

1. 📝 Data Construction: Synthesizes search paths from real multimodal files through a diverge-converge mechanism, establishes static sandboxes with controllable signal-to-noise ratios, and generates queries via reverse engineering
2. 🤖 DR³-Agent: Hierarchical multi-agent architecture
3. 📊 Evaluation Protocol: A multi-dimensional metric suite that comprehensively evaluates evidence retrieval and report generation performance

Figure 3. DR³-Eval framework overview. Includes data construction, DR³-Agent multi-agent system, and multi-dimensional evaluation protocol.

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📊 Dataset Statistics

• 100 independent tasks (50 English + 50 Chinese)
• 3 major domains, 13 sub-domains
• 68% of tasks involve multimodal input
• Average of 2.24 user files per task, up to 6
• Sandbox corpus contains an average of 465.5 web pages under the 512k configuration

Figure 4. Dataset statistics. (a) Domain distribution. (b) File type distribution. (c) Distribution of user files per task.

File Type Distribution

Category	Count	Subtypes
Document	103	PDF(76), Markdown(10), Word(7), Text(5), PPT(5)
Image	62	PNG(37), JPEG(21), WebP(4)
Video	31	MP4(31)
Audio	4	MP3(4)
Data	14	CSV(7), Excel(7)
Other	10	HTML(10)
Total	224	-

Language Distribution

Language	Cases	Avg Files/Case	Avg Multimodal Files/Case
Chinese	50	1.82	0.82
English	50	2.66	1.12
Total	100	2.24	0.97

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🧩 Dataset Structure

DR3-Eval/
├── datasets_en/
│   ├── query.jsonl          # English task queries (50 tasks)
│   ├── 001/                 # Task folder with user files
│   │   ├── file1.pdf
│   │   ├── file2.mp4
│   │   └── ...
│   ├── 002/
│   └── ...
├── datasets_zh/
│   ├── query.jsonl          # Chinese task queries (50 tasks)
│   ├── 001/
│   └── ...
└── README.md

Each task folder contains the user-provided multimodal files referenced in the corresponding query. The query.jsonl file contains the task queries with the following fields:

Field	Description
task	Task ID (e.g., "001")
query	The natural language research query
user_files	List of user-provided file names

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📐 Evaluation Metrics

Dimension	Metric	Description
Information Retrieval	IR (Information Recall)	Coverage of key insights from user files and sandbox corpus
Information Retrieval	CC (Citation Coverage)	Extent to which the report cites necessary source documents
Report Generation	FA (Factual Accuracy)	Factual correctness of cited claims in the report
Report Generation	IF (Instruction Following)	Whether the report satisfies all requirements in the query
Report Generation	DQ (Depth Quality)	Analytical depth and logical rigor of the report

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🚀 Quick Start

📥 Download

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="NJU-LINK/DR3-Eval",
    repo_type="dataset",
    local_dir="./DR3-Eval"
)

📖 Load Queries

import json

# Load English queries
with open("DR3-Eval/datasets_en/query.jsonl") as f:
    en_queries = [json.loads(line) for line in f]

# Load Chinese queries
with open("DR3-Eval/datasets_zh/query.jsonl") as f:
    zh_queries = [json.loads(line) for line in f]

print(f"English tasks: {len(en_queries)}")
print(f"Chinese tasks: {len(zh_queries)}")

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📝 Citation

If you find this work useful, please cite:

@article{dr3eval2026,
  title={DR3-Eval: Towards Realistic and Reproducible Deep Research Evaluation},
  author={},
  journal={arXiv preprint arXiv:xxxx.xxxxx},
  year={2026}
}

🌟 License

This project is licensed under the Apache License 2.0.