[Submitted on 20 Sep 2026]
Title:TRACE: Deployable Tree-Relational Structure Enhancement for Oncology LLMs
View a PDF of the paper titled TRACE: Deployable Tree-Relational Structure Enhancement for Oncology LLMs, by Jizheng Lai and 3 other authors
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Abstract:Large language models are increasingly used in oncology applications, but their predictions are often weakly grounded in explicit medical structure. We present TRACE, a deployable tree-relational enhancement framework for oncology LLMs. TRACE separates expensive offline structure learning from lightweight online inference: oncology concepts and relations are organized into an updatable tree-relational structure, refined using LM-loss-derived evidence, and retrieved at inference time as compact prompt evidence. This design supports task-adaptive evidence selection without requiring supervised labels in the zero-shot setting. Across ten oncology classification tasks and one MedQuAD CancerGov QA benchmark, TRACE improves both label-free evaluation and supervised fine-tuning. Additional analyses show that TRACE improves over vanilla RAG and generic GraphRAG, remains useful under leakage-controlled METABRIC inputs, and produces interpretable evidence paths aligned with clinical reasoning. These results suggest that explicit, updatable medical structure is a practical path toward more accurate and auditable oncology LLM deployment.
Comments: 18 pages, 2 figures, 19 tables. Accepted to the EMNLP 2026 Industry Track for oral presentation
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.35810 [cs.CL]
(or arXiv:2609.35810v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.35810
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jizheng Lai [view email] [v1] Sun, 20 Sep 2026 07:37:08 UTC (5,218 KB)
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