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

2026-08-06

AI Summary

Multimodal multi-agent Retrieval-Augmented Generation (mRAG) systems face significant challenges including high communication overhead, substantial computational costs, and limited scalability. To address these issues, this work proposes M³Prune, a novel framework that achieves the first unified approach to both intra- and cross-modal collaborative pruning. By constructing a hierarchical cooperative communication graph and integrating graph sparsification, modality alignment scoring, and a progressive edge-pruning strategy, M³Prune effectively eliminates redundant communication edges while preserving task performance and substantially reducing computational demands. Experimental results demonstrate that M³Prune outperforms existing single-agent and multi-agent methods on both general and domain-specific mRAG benchmarks, achieving significant reductions in token consumption without compromising accuracy.

Abstract

Recent advances in multi-modal retrieval-augmented generation (mRAG), which augments multi-modal large language models (MLLMs) with external knowledge, have shown that collective intelligence from multiple agents can outperform a single model through effective communication. Despite their strong performance, existing multi-agent systems incur substantial token overhead and computational cost, posing challenges for large-scale deployment. To address these issues, we propose a Multi-Modal Multi-agent hierarchical communication graph PRUNING framework, termed M3Prune. M3Prune eliminates redundant communication edges both across and within modalities, improving the trade-off between task performance and token overhead. Specifically, M3Prune first performs intra-modal graph sparsification in the textual and visual modalities to identify task-critical communication links. It then constructs an inter-modal communication graph and sparsifies cross-modal connections while encouraging consistent cross-modal reasoning through a modality alignment score. Finally, it progressively prunes redundant edges to obtain an efficient hierarchical topology. Extensive experiments on both general-domain and domain-specific mRAG benchmarks show that M3Prune consistently outperforms single-agent and strong multi-agent mRAG systems while signifi- cantly improving token efficiency.

Problem

• multi-modal retrieval-augmented generation
• multi-agent systems
• token overhead
• computational cost
• large-scale deployment

Innovation

• multi-agent pruning
• multi-modal RAG
• hierarchical communication graph
• token efficiency
• modality alignment