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.