TAGS: A Test-Time Generalist-Specialist Framework with
  Retrieval-Augmented Reasoning and Verification
Authors: name: Jianghao Wu, name: Feilong Tang, name: Yulong Li, name: Ming Hu, name: Haochen Xue, name: Shoaib Jameel, name: Yutong Xie, name: Imran Razzak

Recent advances such as Chain-of-Thought prompting have significantly
improved large language models (LLMs) in zero-shot medical reasoning. However,
prompting-based methods often remain shallow and unstable, while fine-tuned
medical LLMs suffer from poor generalization under distribution shifts and
limited adaptability to unseen clinical scenarios. To address these
limitations, we present TAGS, a test-time framework that combines a broadly
capable generalist with a domain-specific specialist to offer complementary
perspectives without any model fine-tuning or parameter updates. To support
this generalist-specialist reasoning process, we introduce two auxiliary
modules: a hierarchical retrieval mechanism that provides multi-scale exemplars
by selecting examples based on both semantic and rationale-level similarity,
and a reliability scorer that evaluates reasoning consistency to guide final
answer aggregation. TAGS achieves strong performance across nine MedQA
benchmarks, boosting GPT-4o accuracy by 13.8%, DeepSeek-R1 by 16.8%, and
improving a vanilla 7B model from 14.1% to 23.9%. These results surpass several
fine-tuned medical LLMs, without any parameter updates. The code will be
available at https://github.com/JianghaoWu/TAGS.