---
title: "One Panel Does Not Fit All — paper by Lu et al. (2026)"
description: "They introduce CAMP (Case-Adaptive Multi-agent Panel). 33 references. Among them: 0."
authors: ["Yuxing Lu", "Yushuhong Lin", "Jason Zhang"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)"
source: "https://aclanthology.org/2026.acl-srw.75/"
doi: "10.18653/v1/2026.acl-srw.75"
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/lu-2026-one-panel-does-not-fit/"
---
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# One Panel Does Not Fit All: Case-Adaptive Multi-Agent Deliberation for Clinical Prediction

**Authors:** [Yuxing Lu](https://md.research.elamur.ai/free/en/authors/yuxing-lu/), Yushuhong Lin and Jason Zhang · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)](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-srw.75/) · [PDF](https://aclanthology.org/2026.acl-srw.75.pdf) · [DOI 10.18653/v1/2026.acl-srw.75](https://doi.org/10.18653/v1/2026.acl-srw.75) · [OpenAlex](https://openalex.org/W7166853440)

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

## Summary
When LLMs are used for clinical prediction, some patient cases are robust to changes in prompt or perspective, while harder, ambiguous ones give unstable answers. The authors argue that fixed inference schemes fail these harder cases. Single-agent sampling draws from only one role, and existing multi-agent systems keep the same roles for every case and simply count votes. They introduce CAMP (Case-Adaptive Multi-agent Panel). An attending-physician agent first makes its own judgment, then selects a small specialist panel for each case and gives each specialist a case-specific focus. Specialists cite evidence from the note and vote KEEP, REFUSE or NEUTRAL on every candidate diagnosis. A router then accepts clear consensus, falls back to the attending's view when most specialists abstain, and sends real conflicts to arbitration, where argument quality matters more than vote count. Using four LLM backbones, the authors evaluated CAMP on two MIMIC-IV tasks: predicting diagnoses and writing brief hospital course (BHC) summaries. It beat strong baselines and used fewer tokens than most multi-agent rivals. Its voting and arbitration records also make decisions auditable.

## Key points
- Data: English, de-identified MIMIC-IV notes from a single U.S. center. The tasks are diagnosis prediction and BHC generation, and BHC quality was judged by LLM ranking rather than by clinicians.
- Three-valued voting lets specialists abstain outside their expertise. By default the panel has k=3 specialists, a size chosen through sensitivity analysis.
- On Llama-3.1-70B, CAMP reached the highest Macro F1 of 87.50 using about 15.5K tokens per note. Majority Voting scored 85.84 with about 6.3K tokens.
- Self-Consistency used about 19K tokens yet barely improved on Single Agent (about 1.9K tokens). MedAgents, LLM-as-a-Judge and Devil's Advocate needed 17–20K tokens.
- The gains shrink when the base model already performs strongly. CAMP is presented as a research prototype, not a tool for autonomous clinical decisions.

*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

- [Peking University](https://md.research.elamur.ai/free/en/universities/peking-university/) — University, Beijing, China · 244 works · 39,359 citations
- [Georgia Institute of Technology](https://md.research.elamur.ai/free/en/universities/georgia-institute-of-technology/) — University, Atlanta, United States · 133 works · 24,497 citations

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## Cite

Cite as: Yuxing Lu, Yushuhong Lin and Jason Zhang. 2026. One Panel Does Not Fit All: Case-Adaptive Multi-Agent Deliberation for Clinical Prediction. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop).

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

```bibtex
@inproceedings{lu2026one,
  title = {{One Panel Does Not Fit All: Case-Adaptive Multi-Agent Deliberation for Clinical Prediction}},
  author = {Yuxing Lu, Yushuhong Lin and Jason Zhang},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-srw.75},
  url = {https://doi.org/10.18653/v1/2026.acl-srw.75}
}
```

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- [next → One Retrieval to Cover Them All: Co-occurrence-Aware Knowledge Base Reorganization for Session-Level RAG](https://md.research.elamur.ai/free/en/papers/ratnakar-2026-one-retrieval-cover-them-all/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/38/)

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*Updated 1 November 2026*