TRACE: Deployable Tree-Relational Structure Enhancement for Oncology LLMs

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.

18 pages, 2 figures, 19 tables. Accepted to the EMNLP 2026 Industry Track for oral presentation

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2609.35810 [cs.CL]

(or arXiv:2609.35810v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.35810 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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