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Xuanyu Meng

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Open access Aug 2026

Decoding high-order clinical correlations: a knowledge-driven large language model framework for specialized medical decision-making

Specialized thoracic-surgery questions require the integration of multi-factor clinical relationships within text, yet general-purpose large language models (LLMs) may underperform on such exam-style benchmarks. We constructed a DK-LLM agent by embedding curated medical textbook knowledge into a LangChain-based framework to support domain-specific reasoning. The model was tested on a 56-item thoracic-surgery examination question set in a restricted text-only setting without internet browsing or external tools and was compared with generic LLM baselines, three thoracic surgeons, and three non-expert engineers. Examination score and error patterns were assessed. The knowledge-augmented DK-LLM configuration showed an 11.8-point examination-score advantage over the version without the local knowledge base. Commercial LLM-based agents outperformed the open-source baselines and non-expert participants on this question set, whereas experienced thoracic surgeons achieved the highest scores overall; in the ablation analysis, removing the local knowledge base reduced the examination score by 11.8 percentage points. Embedding domain-specific knowledge into LLMs may improve performance on specialized exam-style thoracic-surgery questions on this text-only benchmark. However, the present 56-item evaluation does not establish clinical equivalence, diagnostic accuracy in practice, multimodal competence, or readiness for real-world clinical decision support.

Qian Li, Yongxin Li, Chao Ye et al. · 0 citations
Preprint Aug 2026

EnSI-RAG: Entity-Structure-Indexed Retrieval-Augmented Generation for Long-Document Question Answering

Question answering (QA) over long, connected documents remains challenging because relevant evidence may span multiple entities and their relationships. Existing retrieval-augmented generation (RAG) methods typically index documents as raw chunks and retrieve them through embedding similarity. Their performance degrades when chunk boundaries separate entities from supporting evidence or when a question requires multi-hop reasoning across the corpus. We propose EnSI-RAG (Entity-Structure-Indexed Retrieval-Augmented Generation), a framework that constructs a query-independent, entity-centered index. Each record (e, t, k, v) represents an entity e, its type t, a semantic category k in {property, relation, aspect}, and a value v, while retaining links to the original source passages. At query time, these records serve as retrieval handles, and an LLM synthesizes the retrieved passages into the final answer. This design separates evidence localization from answer synthesis while preserving traceable source evidence. Across Loong and Oolong, EnSI-RAG achieves an average accuracy of 78.24. Relative to the published baseline scores used as references, this is 6.62 points higher, suggesting its effectiveness across these settings. The code is available at https://github.com/RamonMeng/EnSI-RAG.

Xuanyu Meng, Jiashuo Sun, Jash Parekh et al. · 0 citations