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A comprehensive benchmark for evaluating deep research agents on academic survey tasks

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<h1 align="center">ReportBench: Evaluating Deep Research Agents via Academic Survey Tasks</h1>

<h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for the latest update.</h5>

## Overview

**ReportBench** is a comprehensive benchmark for evaluating the factual quality and citation behavior of Deep Research agents. Leveraging expert-authored survey papers as ground truth, ReportBench reverse-engineers domain-specific prompts and provides automated tools to assess both cited and non-cited content.

ReportBench addresses this need by:

- **Leveraging expert surveys**: Uses high-quality, peer-reviewed survey papers from arXiv as gold-standard references.  
- **Reverse-engineering prompts**: Generates task-specific prompts matching each survey’s scope, methods, and temporal constraints.  
- **Automated validation**: Employs a dual-path evaluation to verify citation consistency and factual correctness of non-cited statements.
---

## Benchmark Construction

The dataset construction pipeline consists of four phases:

1. **Survey Paper Identification**  
   - Start from an arXiv metadata snapshot (post-2020).  
   - Filter titles/abstracts for “survey” or “review” and confirm publication status via metadata and LLMs classification.  
   - Retain 600 high-quality, peer-reviewed survey papers.

2. **Fine-Grained Reference Extraction**  
   - Download and parse LaTeX sources to extract all in-text citation commands.  
   - Build a gold-standard set of references mirroring the true citation pattern of each survey.

3. **Prompt Generation**  
   - Reverse-engineer three levels of prompts (sentence, paragraph, detail-rich) via LLMs.  
   - Enforce temporal constraints matching each paper’s publication cutoff.  
   - Add explicit instructions to avoid citing the original survey itself.

4. **Application Domain Distribution**  
   - Classify surveys into ten domains using LLMs.  
   - Perform downsampling while adjusting the distribution to achieve balance, and sample one of the three prompt types to form a 100-task benchmark.

---

## Evaluation Framework

ReportBench’s evaluation workflow consists of two complementary validation procedures:

### 1. Content Quality

- **URL Extraction**: Extract all URL citations from the report, including from the base model and Deep Research outputs.  
- **Normalization and Retrieval**: Normalize and deduplicate URLs, then retrieve the content of each web page.  
- **Document Type Classification**: Use an LLM to determine whether each URL corresponds to a scholarly article and extract its title if applicable.  
- **Title Matching**: Compare the extracted titles against ground-truth references from the expert-authored report and compute an overlap ratio.

### 2.1. Cited Statements

- **Statement Extraction**: Identify all sentences in a generated report containing explicit citations.  
- **Source Retrieval**: Scrape the full text of each cited source.  
- **Semantic Matching**: Use an LLM to locate supporting passages and verify consistency.  
- **Scoring**: Compute a citation alignment score for each report.

### 2.2. Non-Cited Statements

- **Statement Extraction**: Extract factual claims without citations, filtering out common-sense content.  
- **Web-Connected Fact Checking**: Query multiple web-connected LLMs (Gemini Pro and Flash) to independently verify each claim.  
- **Voting Mechanism**: Aggregate judgments via majority vote to compute factual accuracy.

---

## Evaluation Results

We evaluated two Deep Research products alongside their corresponding base LLMs using the ReportBench benchmark. Table 1 summarizes precision, recall, average references per report, citation match rate, cited statement count, non-cited factual accuracy, and non-cited statement count.

| Test Model                   | Precision | Recall | Avg Refs | Cit. Match Rate | Cit. Stmt Count | Non-Cit Acc | Non-Cit Stmt Count |
|------------------------------|----------:|-------:|---------:|----------------:|----------------:|-------------:|--------------------:|
| **OpenAI Deep Research**     |     0.385 |  0.033 |    9.89 |          78.87% |           88.2 |       95.83% |                38.9 |
| **Gemini Deep Research**     |     0.145 |  0.036 |   32.42 |          72.94% |           96.2 |       92.21% |                49.6 |
| gemini-2.5-flash             |     0.237 |  0.012 |    5.47 |          44.88% |           12.1 |       98.52% |                11.5 |
| gemini-2.5-pro               |     0.269 |  0.010 |    4.27 |          59.24% |            6.58|       96.08% |                 9.35|
| o3                           |     0.299 |  0.031 |   12.26 |          31.43% |           16.16|       82.22% |                11.51|
| claude4-sonnet           |     0.337 |  0.021 |    6.74 |          73.67% |           14.93|       92.64% |                17.07|

*Table 1. Performance metrics of Deep Research products and their base models.*

#### Product-Level  
- **OpenAI Deep Research**: Highest precision (0.385) and citation match rate (78.87%), indicating focused and accurate retrieval with fewer references.  
- **Gemini Deep Research**: Generates many more citations (32.42 vs. 9.89) but yields only marginal recall gain, suggesting over-generation without proportional coverage benefits.

#### Model-Level  
- **OpenAI vs. o3**: Comparable retrieval metrics, but Deep Research produces far more cited statements (88.2 vs. 16.16) and non-cited statements (38.9 vs. 11.51), and achieves much higher alignment (78.87% vs. 31.43%) and accuracy (95.83% vs. 82.22%).  
- **Gemini Deep Research vs. gemini-2.5-pro**: Trades off precision (0.145 vs. 0.269) for higher recall and citation volume, while maintaining strong alignment (72.94% vs. 59.24%) but slightly lower non-cited statement accuracy.  
- **claude4-sonnet**: Most balanced baseline—moderate precision (0.337), recall (0.021), citation consistency (73.67%), and non-cited statement accuracy (92.64%).

---

## 🛠️ Installation and Usage

### Prerequisites

**Environment Requirements:**
- Python 3.8+
- Required Python packages (install via pip)

**Install Dependencies:**
```bash
pip install pandas pyyaml langchain-openai tenacity tqdm requests pathlib beautifulsoup4 firecrawl-py python-dotenv
```

**API Keys Setup:**
Create a `.env` file in the project root with the following configurations:

```bash
# OpenAI Configuration
OPENAI_PROVIDER=openai  # or "azure" for Azure OpenAI
OPENAI_API_KEY=your_openai_api_key_here
OPENAI_MODEL_NAME=gpt-4o-mini
TEMPERATURE=0.0
MAX_TOKENS=8192

# Azure OpenAI (if using Azure)
AZURE_OPENAI_ENDPOINT=your_azure_endpoint
AZURE_OPENAI_API_VERSION=2023-12-01-preview
AZURE_OPENAI_DEPLOYMENT_NAME=your_deployment_name
AZURE_OPENAI_API_KEY=your_azure_api_key

# Web Scraping (Required for citation verification)
FIRECRAWL_API_KEY=your_firecrawl_api_key

# Search API (Optional, for enhanced fact-checking)
SERPAPI_API_KEY=your_serpapi_key
```

## Project Structure

```
ReportBench/
|
├── 📁 ReportBench Release v1.1
│   ├── ReportBench_v1.1.jsonl      # Main dataset file in JSON Lines format
│   └── ReportBench_v1.1_GT         # Ground truth reference data
|
├── 📁 Core Processing Scripts
│   ├── openai_processor.py          # Process OpenAI Deep Research outputs
│   ├── gemini_processor.py          # Process Gemini Deep Research outputs  
│   ├── statement_evaluator.py       # Extract and evaluate factual statements
│   ├── related_work_evaluator.py    # Evaluate citation accuracy and recall
│   └── metrics_calculator.py        # Calculate final performance metrics
│
├── 📁 Configuration & Utilities
│   ├── config.py                    # API keys and model configurations
│   ├── utils.py                     # Common utilities (LLM clients, CSV ops)
│   ├── cache_utils.py               # URL caching and normalization
│   └── .env                         # Environment variables (create this)
│
├── 📁 Evaluation Modules
│   ├── statement/                   # Statement extraction and verification
│   │   ├── extract_citations.py    # Extract cited statements
│   │   ├── extract_no_citations.py # Extract non-cited statements
│   │   ├── scrape_content.py       # Web scraping for citation sources
│   │   ├── match_text.py           # Semantic matching of statements
│   │   ├── verify_alignment.py     # Verify citation-statement alignment
│   │   └── verify_no_citations_web.py # Web-based fact-checking
│   └── process/                     # Data processing utilities
│       ├── extract_activity_structured.py
│       ├── extract_reference_structured.py
│       └── html2markdown.py
│
├── 📁 Scripts & Templates
│   ├── run_test.sh                  # Main evaluation pipeline
│   ├── process_prompt.py            # Prompt processing utilities
│   └── prompt_template/             # Evaluation prompt templates
│
└── 📄 Configuration Files
    ├── README.md                    # Project documentation
    ├── .gitignore                   # Git ignore rules
    └── .env.example                 # Environment variables template
```

**Key Components:**

- **Processing Pipeline**: `openai_processor.py` → `statement_evaluator.py` + `related_work_evaluator.py` → `metrics_calculator.py`
- **Input Format**: Model outputs as JSON files with `response`/`content` field containing the generated survey text
- **Output**: Comprehensive evaluation metrics including citation alignment scores and factual accuracy rates

## Quick Start

### 1. Prepare Your Model Data

**Important Note:** The data preparation process differs based on your model type:

#### For OpenAI Deep Research & Gemini Deep Research (Web-based)
These are web-based products that require special data collection:

1. **Use Chrome Extension**: Since these are web interfaces, you need to use a Chrome extension to capture the conversation records
2. **Process with Dedicated Scripts**: Use the corresponding processor to parse the captured data:
   ```bash
   # For OpenAI Deep Research
   python openai_processor.py --input=captured_data_dir --output=parsed_output_dir --markdown
   
   # For Gemini Deep Research  
   python gemini_processor.py --input=captured_data_dir --output=parsed_output_dir
   ```

#### For Other Models (API/Local Models)
**Input Format:** Your model outputs should be saved as JSON files with the following structure:

```json
{
  "response": "Your model's generated survey text here...",
  "arxiv_id": "2024.12345",  // Optional: will be extracted from filename if not present
  "query": "Original query prompt",  // Optional
  // ... other metadata fields
}
```

**Alternative accepted field names for the main content:**
- `response`, `content`, `text`, `message`, `output`, `result`

**File Organization:**
```bash
# Create your model's output directory
mkdir -p your-model-name

# Save each evaluation result as: {arxiv_id}.json
# Example filenames:
your-model-name/2003.00653.json
your-model-name/2004.05937.json
# ... (100 files total for the full benchmark)
```

### 2. Run Evaluation Pipeline

```bash
# 1. Process raw outputs (ONLY for web-based Deep Research products)
# For OpenAI Deep Research:
python openai_processor.py --input=data/test_data/raw_data/your-model-name --output=your-model-name-parsed --markdown
# For Gemini Deep Research:
python gemini_processor.py --input=data/test_data/raw_data/your-model-name --output=your-model-name-parsed --markdown
# For other models: Skip this step, use your JSON files directly

# 2. Extract and evaluate statements
python statement_evaluator.py your-model-name-parsed --output-dir your-model-name-stat-results

# 3. Evaluate citation accuracy  
python related_work_evaluator.py --survey-dir your-model-name-parsed --ground-truth-dir ReportBench_v1.1_GT --result-dir your-model-name-related-work-results

# 4. Calculate final metrics
python metrics_calculator.py your-model-name-stat-results
```

### 3. View Results

Results will be saved in structured directories:
- **Statement Evaluation**: `your-model-name-stat-results/` - Contains citation alignment and factual accuracy scores
- **Related Work Evaluation**: `your-model-name-related-work-results/` - Contains precision/recall for citation discovery  
- **Final Metrics**: Summary CSV files with aggregated performance metrics

**Key Output Files:**
- Individual paper results in `{arxiv_id}/` subdirectories

### 4. HuggingFace Dataset (Optional)

You can easily access ReportBench with the following code:

```python
from datasets import load_dataset
dataset = load_dataset("ByteDance-BandAI/ReportBench")
```

## Citation

```bibtex
@misc{li2025reportbenchevaluatingdeepresearch,
      title={ReportBench: Evaluating Deep Research Agents via Academic Survey Tasks}, 
      author={Minghao Li and Ying Zeng and Zhihao Cheng and Cong Ma and Kai Jia},
      year={2025},
      eprint={2508.15804},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2508.15804}, 
}
```

ReportBench

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