SPAR: Scholar Paper Retrieval with LLM-based Agents for Enhanced Academic Search

Paper URL: arxiv:2507.15245

Dataset URL: SPARBench

An intelligent academic paper retrieval system based on Large Language Model (LLM) agents, providing search, retrieval, and re-ranking capabilities.

🚀 Quick Start

Requirements

pip install -r requirements.txt

Basic Configuration

1. Configure API Keys

   • Edit global_config.py to set GPT-related keys and search parameters
   • For local models, refer to local_request_v2.py to configure MODEL_CONFIGS

2. Launch Web Interface

   python3 demo_app_with_front.py

   

   search result details can be found: here

3. Use Service Interface

   python3 run_spr_agent.py $benchname

   Supported benchname: OwnBenchmark | AutoScholarQuery

Project Structure

File	Description
search_engine.py	Main entry point for retrieval system
pipeline_spar.py	Complete SPAR processing pipeline
search_node.py	Specific functionality implementation for pipeline
rerank.py	Result re-ranking module
global_config.py	Global configuration file
demo_app_with_front.py	Visual frontend application

🔧 Advanced Configuration

Local Database Acceleration (Optional)

Download local cache database to improve query speed:

# Download database file
mkdir -p database
wget "http://flagchat.ks3-cn-beijing.ksyuncs.com/shixiaofeng/project/SPAR/arxiv_data.db?KSSAccessKeyId=AKLTkqVnZwpfTBiiu7O6iQHnA&Expires=7753081204&Signature=gaj8%2F5rJ%2BUQWp6wSr0f5KKuJdqs%3D" -O database/arxiv_data.db

Graphical Visualization (Optional)

Install Graphviz to generate tree diagrams of the retrieval process:

# Ubuntu/Debian
sudo apt-get install graphviz
pip install graphviz

# macOS
brew install graphviz
pip install graphviz

# Windows
# 1. Download and install Graphviz: https://graphviz.org/download/
# 2. pip install graphviz

Preview:

📈 Experimental Results

Output Description

• Retrieval results saved to: ./figs/search_results_2025-07-22.json
• Visualization charts saved in ./figs/ directory

Features

🎯 Advanced Search Mode

• Query Rewriting: Automatic query expansion and refinement
• Intent Analysis: Understanding search intent for better results
• Reference Search: Follow citation networks for comprehensive coverage
• Advanced Re-ranking: Multi-layer relevance scoring

⚡ Simple Search Mode

• Multi-source Search: ArXiv, OpenAlex, PubMed integration
• Basic Re-ranking: Fast relevance scoring
• Batch Processing: Efficient parallel processing

🎨 Web Interface

• Interactive UI: User-friendly search interface
• Real-time Results: Live search progress and results
• Export Options: JSON export for further analysis
• Search Tree Visualization: Visual representation of search process

📖 Citation

If you use the SPAR system, please cite our work:

@misc{shi2025sparscholarpaperretrieval,
      title={SPAR: Scholar Paper Retrieval with LLM-based Agents for Enhanced Academic Search},
      author={Xiaofeng Shi and Yuduo Li and Qian Kou and Longbin Yu and Jinxin Xie and Hua Zhou},
      year={2025},
      eprint={2507.15245},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2507.15245},
}

📄 License

This project is licensed under the MIT License.

🤝 Contributing

Issues and Pull Requests are welcome to help improve the SPAR system!

Troubleshooting

Common Issues

1. API Quota Exceeded: Ensure you have sufficient API quota for LLM calls
2. Slow Performance: Consider using local database acceleration
3. Network Issues: Check network connectivity for external API calls

Performance Tips

• Test on small datasets first to evaluate system performance
• Use local models when possible to reduce API costs
• Enable database caching for frequently accessed papers

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Note: Ensure you have sufficient API quota for Large Language Model calls. It's recommended to test the system performance on small-scale data first.