@inproceedings{wu-etal-2026-tags,
title = "{TAGS}: A Test-Time Generalist{--}Specialist Framework with Retrieval-Augmented Reasoning and Verification",
author = "Wu, Jianghao and
Tang, Feilong and
Li, Yulong and
Hu, Ming and
Xue, Haochen and
Jameel, Shoaib and
Ge, Zongyuan and
Xie, Yutong and
Razzak, Imran",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.757/",
pages = "15428--15445",
ISBN = "979-8-89176-395-1",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T TAGS: A Test-Time Generalist–Specialist Framework with Retrieval-Augmented Reasoning and Verification
%A Wu, Jianghao
%A Tang, Feilong
%A Li, Yulong
%A Hu, Ming
%A Xue, Haochen
%A Jameel, Shoaib
%A Ge, Zongyuan
%A Xie, Yutong
%A Razzak, Imran
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F wu-etal-2026-tags
%X 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.
%U https://aclanthology.org/2026.findings-acl.757/
%P 15428-15445
Markdown (Informal)
[TAGS: A Test-Time Generalist–Specialist Framework with Retrieval-Augmented Reasoning and Verification](https://aclanthology.org/2026.findings-acl.757/) (Wu et al., Findings 2026)
ACL
- Jianghao Wu, Feilong Tang, Yulong Li, Ming Hu, Haochen Xue, Shoaib Jameel, Zongyuan Ge, Yutong Xie, and Imran Razzak. 2026. TAGS: A Test-Time Generalist–Specialist Framework with Retrieval-Augmented Reasoning and Verification. In Findings of the Association for Computational Linguistics: ACL 2026, pages 15428–15445, San Diego, California, United States. Association for Computational Linguistics.