M$^3$Prune: Hierarchical Communication Graph Pruning for Efficient Multi-Modal Multi-Agent Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2511.19969v1
• Date: Tue, 25 Nov 2025 06:29:13 GMT
• Title: M$^3$Prune: Hierarchical Communication Graph Pruning for Efficient Multi-Modal Multi-Agent Retrieval-Augmented Generation
• Authors: Weizi Shao, Taolin Zhang, Zijie Zhou, Chen Chen, Chengyu Wang, Xiaofeng He,
• Abstract summary: We propose a novel Multi-Modal Multi-agent hierarchical communication graph PRUNING framework, termed M$3$Prune.<n>Our framework eliminates redundant edges across different modalities, achieving an optimal balance between task performance and token overhead.<n>Our method consistently outperforms both single-agent and robust multi-agent mRAG systems.
• Score: 18.091284320771006
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in multi-modal retrieval-augmented generation (mRAG), which enhance multi-modal large language models (MLLMs) with external knowledge, have demonstrated that the collective intelligence of multiple agents can significantly outperform a single model through effective communication. Despite impressive performance, existing multi-agent systems inherently incur substantial token overhead and increased computational costs, posing challenges for large-scale deployment. To address these issues, we propose a novel Multi-Modal Multi-agent hierarchical communication graph PRUNING framework, termed M$^3$Prune. Our framework eliminates redundant edges across different modalities, achieving an optimal balance between task performance and token overhead. Specifically, M$^3$Prune first applies intra-modal graph sparsification to textual and visual modalities, identifying the edges most critical for solving the task. Subsequently, we construct a dynamic communication topology using these key edges for inter-modal graph sparsification. Finally, we progressively prune redundant edges to obtain a more efficient and hierarchical topology. Extensive experiments on both general and domain-specific mRAG benchmarks demonstrate that our method consistently outperforms both single-agent and robust multi-agent mRAG systems while significantly reducing token consumption.

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