---
title: "TAGS — paper by Wu et al. (2026) — summary and ideas"
description: "The authors propose TAGS, which runs purely at inference time and changes no model weights. 46 references."
authors: ["Jianghao Wu", "Feilong Tang", "Yulong Li", "Ming Hu", "Haochen Xue", "Shoaib Jameel", "Zongyuan Ge", "Yutong Xie", "Imran Razzak"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.757/"
doi: "10.18653/v1/2026.findings-acl.757"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wu-2026-tags-test-time-generalist-specialist/"
---
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# TAGS: A Test-Time Generalist–Specialist Framework with Retrieval-Augmented Reasoning and Verification

**Authors:** Jianghao Wu, [Feilong Tang](https://md.research.elamur.ai/free/en/authors/feilong-tang/), Yulong Li, Ming Hu, Haochen Xue, [Shoaib Jameel](https://md.research.elamur.ai/free/en/authors/shoaib-jameel/), [Zongyuan Ge](https://md.research.elamur.ai/free/en/authors/zongyuan-ge/), [Yutong Xie](https://md.research.elamur.ai/free/en/authors/yutong-xie/) and [Imran Razzak](https://md.research.elamur.ai/free/en/authors/imran-razzak/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.757/) · [PDF](https://aclanthology.org/2026.findings-acl.757.pdf) · [DOI 10.18653/v1/2026.findings-acl.757](https://doi.org/10.18653/v1/2026.findings-acl.757) · [OpenAlex](https://openalex.org/W4414581114)

**Citations:** 1 citations (OpenAlex, 2026-10-14)

## Summary
The paper targets a weakness of LLMs in medical question answering. Prompting tricks such as Chain-of-Thought tend to give shallow or unstable reasoning, and fine-tuned medical models struggle when the data distribution shifts. The authors propose TAGS, which runs purely at inference time and changes no model weights. A generalist agent and a domain specialist agent each reason over the question in two rounds, producing four candidate rationale–answer pairs. Two helper components support them:

- **Hierarchical retrieval:** exemplars are first fetched from a frozen medical-QA corpus by question similarity, then re-selected by similarity to the agents' draft rationales.
- **Reasoning consistency evaluator:** an LLM-based verifier scores each candidate, and the highest-scoring answer is chosen.

Across nine MedQA benchmarks, TAGS beats prompting, retrieval and multi-agent baselines on GPT-4o, DeepSeek-R1 and a small 7B model. It sometimes outperforms fine-tuned medical LLMs. This matters because it offers a training-free way to make domain QA more robust when fine-tuning is impractical. Evaluation covers only English multiple-choice tasks.

## Key points
- **Headline gains:** accuracy rises by 13.8% for GPT-4o and 16.8% for DeepSeek-R1. A vanilla 7B model improves from 14.1% to 23.9%.
- **Retrieval setup:** it uses M3-Embedding (1024-dim vectors) with cosine similarity. The default is K = 2 exemplars, and larger K adds noise.
- **Specialist count:** adding one specialist to the generalist lifts MMLU from 34.2% to 45.2% and MedXpert-U from 16.0% to 22.0%. Further specialists bring little extra benefit.
- **Robustness checks:** results stay competitive when the top-10 nearest exemplars are excluded (RAG-w/o-top10). They also hold with the small m1k corpus of 1,000 CoT exemplars.
- **Cost:** with GPT-4o on MedQA, TAGS takes about 72 s per question, compared with 27.7 s for CoT-SC and 109.6 s for Multi-Persona. Code is released at github.com/JianghaoWu/TAGS.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Monash University](https://md.research.elamur.ai/free/en/universities/monash-university/) — University, Melbourne, Australia · 60 works · 54,866 citations
- [Mohamed bin Zayed University of Artificial Intelligence](https://md.research.elamur.ai/free/en/universities/mohamed-bin-zayed-university-of-artificial-intelligence/) — University, Abu Dhabi, United Arab Emirates · 124 works · 5,676 citations
- [University of Southampton](https://md.research.elamur.ai/free/en/universities/university-of-southampton/) — University, Southampton, United Kingdom · 10 works · 2,495 citations
- [Xi’an Jiaotong-Liverpool University](https://md.research.elamur.ai/free/en/universities/xi-an-jiaotong-liverpool-university/) — University, Suzhou, China · 10 works · 319 citations

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## Cite

Cite as: 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. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wu2026tags,
  title = {{TAGS: A Test-Time Generalist–Specialist Framework with Retrieval-Augmented Reasoning and Verification}},
  author = {Jianghao Wu, Feilong Tang, Yulong Li, Ming Hu, Haochen Xue, Shoaib Jameel, Zongyuan Ge, Yutong Xie and Imran Razzak},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.757},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.757}
}
```

- [← previous TagRAG: Tag-guided Hierarchical Knowledge Graph Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/tao-2026-tagrag-tag-guided-hierarchical-knowledge/)
- [next → Taien at SemEval-2026 Task 9: Multilingual Polarization Detection Using Transformer-based Models](https://md.research.elamur.ai/free/en/papers/taien-2026-taien-semeval-2026-task-9/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/17/)

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*Updated 1 November 2026*