---
title: "CAMEC — paper by Wu et al. (2026) — summary and ideas"
description: "LLMs for Chinese medical question answering still hallucinate, lack solid factual grounding and struggle with clinically… 46 references."
authors: ["Yukang Wu", "Xiyuan Jia", "Jiayi Wu", "HongChen Yu", "Yuhan Qiu", "Wu Guohua"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1781/"
doi: "10.18653/v1/2026.acl-long.1781"
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-camec-complexity-aware-multi-expert/"
---
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# CAMEC: Complexity-Aware Multi-Expert Collaboration for Reliable Chinese Medical Question Answering

**Authors:** Yukang Wu, Xiyuan Jia, [Jiayi Wu](https://md.research.elamur.ai/free/en/authors/jiayi-wu/), HongChen Yu, Yuhan Qiu and Wu Guohua · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/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.acl-long.1781/) · [PDF](https://aclanthology.org/2026.acl-long.1781.pdf) · [DOI 10.18653/v1/2026.acl-long.1781](https://doi.org/10.18653/v1/2026.acl-long.1781) · [OpenAlex](https://openalex.org/W7166829302)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
LLMs for Chinese medical question answering still hallucinate, lack solid factual grounding and struggle with clinically complicated cases. Most earlier systems rely on a single model or a fixed pipeline. CAMEC addresses this in two steps. First, it adapts Qwen3-8B to medicine through three successive LoRA fine-tuning stages: domain adaptation, instruction following and clinical reasoning. Each stage replays earlier data to limit forgetting. Second, at inference a small classifier estimates how difficult a question is and decides which of three experts to call: a chain-of-thought reasoner, a retrieval expert over a dense medical vector store, and a knowledge-graph expert. An LLM judge scores each expert's report for correctness, completeness and safety. It requests revisions for up to three rounds and then merges the reports into a weighted final answer. On four Chinese medical exam benchmarks, the system outperforms general and medical baselines, including HuatuoGPT-o1-7B. Routing reduces the number of expert calls with almost no accuracy loss, which makes reliable clinical QA cheaper to run.

## Key points
- Accuracy: 78.86% on CMExam, 84.15% on MedQA-CN, 78.51% on CMMLU-Med and 74.40% on CMB-exam.
- SFT data: Huatuo26M-Lite (doctor–patient dialogues), HuatuoGPT2-SFT-GPT4-140K (multi-turn consultations) and medical-o1-reasoning-SFT (CoT case analyses). An LLM filter removed non-medical samples.
- Router: a Qwen3-0.6B 3-way classifier (LOW/MEDIUM/HIGH). It was trained on 8,000 CMExam and MedQA-CN questions labelled by DeepSeek-R1 with a Medical Complexity Score. The score combines four weighted factors: how ambiguous the symptoms are, how rare the disease is, the likelihood of comorbidities, and how uncertain the diagnosis is. LOW queries use CoT only, MEDIUM adds RAG, and HIGH adds KG.
- Efficiency on MedQA-CN: the average number of experts per query drops from 3.00 to 2.32, cutting inference cost by 22.51%. Accuracy falls only from 84.36% to 84.15%.
- Ablation on CMExam: the base model reaches 72.30%, hierarchical SFT alone 73.91%, and experts without SFT 77.14%. The full system reaches 78.86%, and every two-expert combination scores lower.

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

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- [Leiden University](https://md.research.elamur.ai/free/en/universities/leiden-university/) — University, Leiden, Netherlands · 37 works · 1,408 citations
- [Hangzhou Dianzi University](https://md.research.elamur.ai/free/en/universities/hangzhou-dianzi-university/) — University, Hangzhou, China · 15 works · 1,061 citations

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## Cite

Cite as: Yukang Wu, Xiyuan Jia, Jiayi Wu, HongChen Yu, Yuhan Qiu and Wu Guohua. 2026. CAMEC: Complexity-Aware Multi-Expert Collaboration for Reliable Chinese Medical Question Answering. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

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

```bibtex
@inproceedings{wu2026camec,
  title = {{CAMEC: Complexity-Aware Multi-Expert Collaboration for Reliable Chinese Medical Question Answering}},
  author = {Yukang Wu, Xiyuan Jia, Jiayi Wu, HongChen Yu, Yuhan Qiu and Wu Guohua},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1781},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1781}
}
```

- [← previous CaM-HG: Causal-Enhanced MoE and Hypergraphs Network for Incomplete Multimodal Emotion Recognition in Conversations](https://md.research.elamur.ai/free/en/papers/yang-2026-cam-hg-causal-enhanced-moe/)
- [next → CAMMAR: Culture-Aware Matryoshka for Metaphorical Arabic Representations](https://md.research.elamur.ai/free/en/papers/awinat-2026-cammar-culture-aware-matryoshka-metaphorical/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/25/)

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*Updated 2 November 2026*