Author

Yingyun Li

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#artificial intelligence Preprint Sep 2026

TRACE: Deployable Tree-Relational Structure Enhancement for Oncology LLMs

TRACE is presented, a deployable tree-relational enhancement framework for oncology LLMs that separates expensive offline structure learning from lightweight online inference, and suggests that explicit, updatable medical structure is a practical path toward more accurate and auditable oncology LLM deployment.

Ji-Zheng Lai, Ying-Yun Li, Ying Qin et al. · 0 citations

#artificial intelligence Review May 2026

Key Coverage Matters: Semi-Structured Extraction of OCR Clinical Reports

This work forms a canonical key-conditioned extractive question answering over OCR-derived clinical reports and introduces key coverage as a metric to quantify inventory completeness, showing that key coverage is a dominant factor for end-to-end performance.

Yu Wang, Yingyun Li, Ying Qin et al. · arXiv.org · 0 citations