TAGS: A Test-Time Generalist-Specialist Framework with
  Retrieval-Augmented Reasoning and Verification
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.
68358855d34b8e5da4e82fe6
Jianghao Wu
claimed_verified
2025-05-27T10:03:31.484Z
66de7dcb21f901b049895e33
/avatars/a8beb6ef83afae6e40fdcf3a1fb08a62.svg
Jianghao
user
JianghaoWu
68358855d34b8e5da4e82fe7
Feilong Tang
admin_assigned
2025-05-27T12:39:04.112Z
67467d9bf7b7f6fffeee8f09
/avatars/da5dd1acd79478540c32b34060710e1b.svg
tangfeilong
user
tfl01
68358855d34b8e5da4e82fe8
Yulong Li
68358855d34b8e5da4e82fe9
Ming Hu
68358855d34b8e5da4e82fea
Haochen Xue
68358855d34b8e5da4e82feb
Shoaib Jameel
68358855d34b8e5da4e82fec
Yutong Xie
68358855d34b8e5da4e82fed
Imran Razzak
admin_assigned
2025-05-27T12:39:16.051Z
65ade1c114d782df06d92798
/avatars/f47dffe30dd2af2ae774309fea017efe.svg
Imran Razzak
user
imranrazzakunsw
TAGS: A Test-Time Generalist-Specialist Framework with
  Retrieval-Augmented Reasoning and Verification
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.
TAGS, a test-time framework combining generalist and specialist models with hierarchical retrieval and reliability scoring, enhances medical LLM reasoning without fine-tuning.
Chain-of-Thought prompting large language models (LLMs) zero-shot medical reasoning fine-tuning parameter updates hierarchical retrieval mechanism reliability scorer MedQA benchmarks GPT-4o DeepSeek-R1