arXiv AI August 11, 2026 By Weizi Shao, Taolin Zhang, Zijie Zhou, Chen Chen, Chengyu Wang, Xiaofeng He

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

arXiv:2511. 19969v2 Announce Type: replace 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 2

MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation

arXiv:2606. 00610v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become an essential method for mitigating hallucinations in Large Language Models (LLMs) by leveraging external knowledge.

By Chuanjie Wu, Zhishang Xiang, Yunbo Tang, Zerui Chen, Qinggang Zhang, Jinsong Su

llms rag agents benchmarks safety

arXiv AI

Jul 15

PFAdapter: Hierarchical LoRA Decomposition for Personalized Federated MLLMs

arXiv:2607. 12111v1 Announce Type: cross Abstract: Agentic AI systems are reshaping communications and networking by deploying autonomous intelligent agents capable of collaborative learning while maintaining data privacy at network edges.

By Jing Liu, Kun Yang, Yan Wang, Dingkang Yang, Xiaoshuai Hao, Wei Zhang, Yang Liu, Wei Zhou

llms agents fine-tuning multimodal benchmarks safety

arXiv AI

Jul 14

MG$^2$-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation

arXiv:2604. 04969v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) mitigates hallucinations in Multimodal Large Language Models (MLLMs), yet existing systems struggle with complex cross-modal reasoning.

By Sijun Dai, Qiang Huang, Xiaoxing You, Jun Yu

llms rag multimodal benchmarks safety

arXiv AI

Jun 2

MOC: Multi-Order Communication in LLM-based Multi-Agent Systems

arXiv:2606. 02359v1 Announce Type: new Abstract: Despite the remarkable progress of Large Language Model (LLM) based Multi-Agent Systems, most research focuses on optimizing coordination topology while largely underexploring the equally critical problem: how to transmit and optimize messages among agents effectively?

By Yao Guan, Lin Wang, Zhihu Lu, Ziyi Wang, Wenzhu Yan, Qiang Duan

llms agents

arXiv Machine Learning

Jul 22

Node-as-Agent: Graph Agentic Network

arXiv:2508. 00429v5 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have achieved remarkable success in graph-based learning by propagating information among neighbor nodes via predefined aggregation mechanisms.

By Minghao Guo, Xi Zhu, Qingyue Jiao, Xiujin Liu, Haochen Xue, Chong Zhang, Shuhang Lin, Jingyuan Huang, Ziyi Ye, Yongfeng Zhang

llms rag agents fine-tuning

arXiv AI

Jul 28

Focus Is All You Need: Adaptive Goal-aware Attention Orchestration for Multi-Agent Graph Systems

arXiv:2607. 23678v1 Announce Type: new Abstract: Large language models (LLMs) enable autonomous agents for reasoning, planning, and tool use.

By Mingzhou Fan, Siyuan Xu, Mingxuan Yuan

llms agents

Related stories

MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation

PFAdapter: Hierarchical LoRA Decomposition for Personalized Federated MLLMs

MG$^2$-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation

MOC: Multi-Order Communication in LLM-based Multi-Agent Systems

Node-as-Agent: Graph Agentic Network

Focus Is All You Need: Adaptive Goal-aware Attention Orchestration for Multi-Agent Graph Systems