---
title: "Beyond Single View — paper by Xu et al. (2026)"
description: "The authors present MedMultiBench, a benchmark built specifically to measure how well models reason over several medical… 40 references."
authors: ["Dexuan Xu", "Yuan Jiayin", "Jianing Wang", "Yanyuan Chen", "Hanpin Wang", "Yu Huang"]
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.1263/"
doi: "10.18653/v1/2026.acl-long.1263"
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/xu-2026-beyond-single-view-comprehensive-benchmark/"
---
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# Beyond Single View: A Comprehensive Benchmark for Medical Multimodal Large Language Models on Multi-Image Understanding

**Authors:** [Dexuan Xu](https://md.research.elamur.ai/free/en/authors/dexuan-xu/), Yuan Jiayin, [Jianing Wang](https://md.research.elamur.ai/free/en/authors/jianing-wang/), Yanyuan Chen, Hanpin Wang and [Yu Huang](https://md.research.elamur.ai/free/en/authors/yu-huang/) · **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.1263/) · [PDF](https://aclanthology.org/2026.acl-long.1263.pdf) · [DOI 10.18653/v1/2026.acl-long.1263](https://doi.org/10.18653/v1/2026.acl-long.1263) · [OpenAlex](https://openalex.org/W7166875651)

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

## Summary
Medical multimodal LLMs are usually tested on one image at a time, but clinicians routinely combine evidence from adjacent slices, follow-up scans, and different modalities. The authors present MedMultiBench, a benchmark built specifically to measure how well models reason over several medical images at once. It contains 11,392 expert-verified samples in 13 subtasks, organized into four dimensions: Joint Reasoning, Comparative Analysis, Comprehensive Perception (for example, spatial ordering and temporal change), and In-Context Learning. The samples were assembled from public resources and passed through filtering, label alignment, and LLM-assisted text rewriting, with medical professionals checking the results. The authors evaluated 13 state-of-the-art models. They found that strong single-image performance does not carry over reliably to multi-image settings. Open-source models deteriorate as more images are added, while closed-source systems such as Gemini3-Pro stay largely stable. The benchmark exposes a practical weakness in long visual-context processing that matters for real clinical workflows.

## Key points
- **Scale and structure:** MedMultiBench has 11,392 samples across 13 subtasks. Its taxonomy sorts the ways images relate to one another into four kinds: continuity across space and time, heterogeneity between different views, correspondence of differences between images, and reference based on semantic meaning.
- **Data sources:** The samples come from IU-Xray, MedPix, GMAI, OmniMedVQA, MedFrameQA, Med-MIM, and MMMU. GPT-5 rewrote some text stylistically, and all rewritten text was manually verified by medical experts.
- **Effect of image count:** Gemini3-Pro reaches its best accuracy, 69.7%, with 10 input images. Open-source models such as Qwen3-VL-8B and Lingshu-7B often fall below 30% when given many images.
- **Few-shot results:** On IU-Xray, Qwen3-VL-8B and Lingshu-7B score above 80 on BERTScore. On MedPix, Gemini3-Pro leads at every shot count, while some models plateau or slightly decline at 5 shots.
- **Limitations:** Volumes are approximated with sequences of 2D slices rather than native 3D data such as NIfTI. Open-ended answers are scored with Rouge-L and BERTScore, which may not reflect expert clinical judgment.

*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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## Cite

Cite as: Dexuan Xu, Yuan Jiayin, Jianing Wang, Yanyuan Chen, Hanpin Wang and Yu Huang. 2026. Beyond Single View: A Comprehensive Benchmark for Medical Multimodal Large Language Models on Multi-Image Understanding. 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{xu2026beyond,
  title = {{Beyond Single View: A Comprehensive Benchmark for Medical Multimodal Large Language Models on Multi-Image Understanding}},
  author = {Dexuan Xu, Yuan Jiayin, Jianing Wang, Yanyuan Chen, Hanpin Wang and Yu Huang},
  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.1263},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1263}
}
```

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*Updated 2 November 2026*