DeepRAG: Integrating Hierarchical Reasoning and Process Supervision for Biomedical Multi-Hop QA

We propose DeepRAG, a novel framework that integrates DeepSeek hierarchical question decomposition capabilities with RAG Gym unified retrieval-augmented generation optimization using process level supervision. Targeting the challenging MedHopQA biomedical question answering task, DeepRAG systematically decomposes complex queries into precise sub-queries and employs concept level reward signals informed by the UMLS ontology to enhance biomedical accuracy. Preliminary evaluations on the MedHopQA dataset indicate that DeepRAG significantly outperforms baseline models, including standalone DeepSeek and RAG Gym, achieving notable improvements in both Exact Match and concept level accuracy.

@article{ji2025_2506.00671,
  title={ DeepRAG: Integrating Hierarchical Reasoning and Process Supervision for Biomedical Multi-Hop QA },
  author={ Yuelyu Ji and Hang Zhang and Shiven Verma and Hui Ji and Chun Li and Yushui Han and Yanshan Wang },
  journal={arXiv preprint arXiv:2506.00671},
  year={ 2025 }
}