# MedPrune: Topology-Efficient Multimodal Multi-Agent Communication Evolution for Medical VQA Tasks

arXiv: https://arxiv.org/abs/2610.06695 · HTML: https://mlsanity.com/paper/2610.06695/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 21 of its 59 references that are in the map.
- First posted on arXiv 5 Oct 2026.

## Placed in

- Multimodal LLMs / vision-language models / [Medical vision-language models](https://mlsanity.com/mllm/medical-vision-language-models/)
- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 21 of 21)

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- [MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs](https://mlsanity.com/paper/1901.07042/) (2019)
- [HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge](https://mlsanity.com/paper/2304.06975/) (2023)
- [Vision-Language Models for Medical Report Generation and Visual Question Answering: A Review](https://mlsanity.com/paper/2403.02469/) (2024)
- [Medical Hallucinations in Foundation Models and Their Impact on Healthcare](https://mlsanity.com/paper/2503.05777/) (2025)
- [MedAgent-Pro: Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic Workflow](https://mlsanity.com/paper/2503.18968/) (2025)
- [GMAI-VL-R1: Harnessing Reinforcement Learning for Multimodal Medical Reasoning](https://mlsanity.com/paper/2504.01886/) (2025)
- [MMedAgent-RL: Optimizing Multi-Agent Collaboration for Multimodal Medical Reasoning](https://mlsanity.com/paper/2506.00555/) (2025)
- [PeFoMed: Parameter Efficient Fine-tuning of Multimodal Large Language Models for Medical Imaging](https://mlsanity.com/paper/2401.02797/) (2024)
- [Scaling medical imaging report generation with multimodal reinforcement learning](https://mlsanity.com/paper/2601.17151/) (2026)
- [SkillGraph: Self-Evolving Multi-Agent Collaboration with Multimodal Graph Topology](https://mlsanity.com/paper/2604.17503/) (2026)
- [From Conflict to Consensus: Boosting Medical Reasoning via Multi-Round Agentic RAG](https://mlsanity.com/paper/2603.03292/) (2026)
- [CARE: Towards Clinical Accountability in Multi-Modal Medical Reasoning with an Evidence-Grounded Agentic Framework](https://mlsanity.com/paper/2603.01607/) (2026)
- [MARCH: Multi-Agent Radiology Clinical Hierarchy for CT Report Generation](https://mlsanity.com/paper/2604.16175/) (2026)
- [Navigating the Emotion Tree: Hierarchical Hyperbolic RAG for Multimodal Emotion Recognition](https://mlsanity.com/paper/2605.18884/) (2026)
- [Cross-Modal Memory Compression for Efficient Multi-Agent Debate](https://mlsanity.com/paper/2602.00454/) (2026)
- [M3Prune: Hierarchical Communication Graph Pruning for Efficient Multi-Modal Multi-Agent Retrieval-Augmented Generation](https://mlsanity.com/paper/2511.19969/) (2025)
- [Dual-Kernel Adapter: Expanding Spatial Horizons for Data-Constrained Medical Image Analysis](https://mlsanity.com/paper/2602.18888/) (2026)
- [ACE-LoRA: Graph-Attentive Context Enhancement for Parameter-Efficient Adaptation of Medical Vision-Language Models](https://mlsanity.com/paper/2603.17079/) (2026)
- [Synthetic Data Generation for Long-Tail Medical Image Classification: A Case Study in Skin Lesions](https://mlsanity.com/paper/2605.03221/) (2026)

## Abstract (arXiv)

While medical multimodal large language models (Med-MLLMs) advance medical visual question answering (VQA), existing clinical workflow-inspired multi-agent frameworks suffer from interaction patterns and excessive computational overhead caused by redundant communication topologies. In this paper, we propose MedPrune, an efficient medical multimodal multi-agent collaboration framework that dynamically prunes both nodes and edges from the communication topology to enhance reasoning ability and token efficiency. Specifically, we first formulate the diagnostic process as a heterogeneous communication graph, where nodes represent specialist agents from various departments and edges capture intra- and inter-departmental interactions. Building on this graph, we introduce two sparsification mechanisms to enable adaptive collaborative evolution: (1) Heterogeneous Node Sparsification, which eliminates task-irrelevant specialist agents irrelevant to the current multimodal question via reinforcement learning-driven topological optimization, and (2) Heterogeneous Edge Sparsification, which selectively retains only the most diagnostically salient intra- and inter-departmental connections by jointly optimizing task performance and topological complexity. Extensive medical VQA experiments under full-set and few-shot training settings prove MedPrune surpasses multi-agent baselines and boosts token efficiency with strong adversarial robustness.