arxiv arXiv cs.AI · 2d ago · research

RareDx uses graph-grounded policy optimization to improve rare-disease diagnosis

from English

The authors introduce RareDx, a system that couples controlled evidence use with knowledge-graph-grounded policy optimization to address the long-tail reasoning problem of rare-disease diagnosis. The training pipeline combines Top-10 post-training with RareDx-KGPO, which projects predictions into a canonical disease graph and integrates curated graded relevance, ontology proximity, biomedical similarity, and phenotype consistency.

Across eight benchmarks, the complete RareDx system centered on Qwen3.5-9B reaches 38.34 macro Hit@10, surpassing GPT-5.5 by 1.60 points under the archived protocol.
A disjoint validation-selection audit retains a 6.80-point routing gain over Direct inference on held-out cases.
The 27B system reaches 23.53/36.56/40.76 at Hit@1/5/10.
Controlled ablations show that retrieval is not uniformly helpful and that controlled routing is central to the gain.

These results indicate that structured medical knowledge can turn a compact model into a competitive diagnostic ranker across heterogeneous long-tail settings in clinical practice.

Importance 2/3 New harness with differentiators arXiv cs.AI Alibaba (Qwen) Research paper Retrieval & RAG

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