Multi-Agent LLM Systems

LLM: Large Language Model

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85 papers in the last four weeks, up 107% on the four weeks before. 0.8% of all new papers.

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Concealing LLM-Based Multi-Agent Topology via Phantom Structure Injection
Sep 29, 2026Longzhu He, Zelang Wen, Xinfeng Li +2Multi-Agent LLM Systems Multi-Agent System Security
Nanyang Technological University · Beijing University of Posts and Telecommunications · The Hong Kong Polytechnic University
FlowMAS: Learning Multi-Agent Workflow Topology via Information-guided Generative Flow Network
Sep 29, 2026Haitao Wang, Chenjing Liang, Haipeng Zhang +6Multi-Agent LLM Systems Multi-Agent System Optimization
Fujian Key Laboratory of Urban Intelligent Sensing and Computing, Xiamen University, Xiamen, China · School of Computer, National University of Defense Technology, China
LLM-Based Multi-Agent Systems over Wireless Networks: A Joint Agent--Network Design Perspective
Sep 29, 2026Chao Hu, Yuan Guo, Guanlin Wu +3Multi-Agent LLM Systems Multi-Agent Systems
College of Computer Science and Software Engineering (CSSE), Shenzhen University, Shenzhen 518060, China · School of Science and Engineering (SSE), the Shenzhen Future Network of Intelligence Institute (FNii), and the Guangdong Provincial Key Laboratory of Future Networks of Intelligence, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518172, China · School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
LatCom: Cross-Agent Latent Compression for Efficient Multi-Agent Collaboration
Sep 29, 2026Shinan Zhang, Tao Zhang, Qihui Zhu +7Multi-Agent LLM Systems Multi-Agent Collaboration
University of Science and Technology of China · Institute of Artificial Intelligence, Hefei Comprehensive National Science Center
When Upstream Messages Override Correct Answers: A Controlled Study of Multi-Agent LLM Collaboration
Sep 29, 2026Yaxin Gong, Gangyi Zhang, Chongming Gao +7Multi-Agent LLM Systems Multi-Agent Collaboration
University of Science and Technology of China · Qwen Business Unit of Alibaba · National University of Singapore
Frontier Autolab: Organizational Memory, Adversarial Dissent and Temporal Leakage in Multi-Agent LLM Firms Across Fifty Years of Technological Change
Sep 29, 2026Bravish GhoshMulti-Agent LLM Systems LLM Agent Memory
Independent researcher
An Exact Generate - Transform Decomposition of Small-LLM Team Scaling Across Orchestration Architectures
Sep 28, 2026Blaz Bertalanic, Carolina FortunaMulti-Agent LLM Systems LLM Agent Orchestration
Jožef Stefan Institute, Ljubljana, Slovenia
Self-Adapting Group of Experts for Multi-Agent Reasoning
Sep 28, 2026Mohammad Atif Quamar, Nurbek Tastan, Karthik Nandakumar +1Multi-Agent LLM Systems Multi-Agent Reasoning
Mohamed bin Zayed University of Artificial Intelligence · Michigan State University · RIKEN AIP
Prompted Identity Degrades Cooperation in Multi-Agent LLM Systems
Sep 28, 2026Xavier Del Giudice, Alessio Palma, Matteo Migliarini +2Multi-Agent LLM Systems Multi-Agent Coordination
Sapienza University of Rome, Italy
Can Generative AI Automate Data Extraction for Meta-Analysis? A Case Study on Intercropping Research
Sep 28, 2026Zehao Lu, Xingguo Xiong, Wopke van der Werf +2Multi-Agent LLM Systems LLM Information Extraction
Wageningen University & Research, Wageningen, the Netherlands · Zhejiang Academy of Agricultural Sciences
MASTraceBench: Diagnosing Collaboration Gains through Proposal Trajectories in LLM-Based Multi-Agent Systems
Sep 28, 2026Yapeng Li, Songze Li, Shuang Yu +4Multi-Agent LLM Systems Multi-Agent Collaboration
Harbin Institute of Technology, Harbin, China · Peking University, Beijing, China
M3OS: A Monte Carlo Graph Search-Orchestrated Multi-Agent LLM System for Evidence-Traced Molecular Optimization
Sep 28, 2026Junjie Wang, Yaowei Jin, Ruohui Tang +7Molecular Optimization Multi-Agent LLM Systems
Lingang Laboratory, Shanghai, 200031, China. · School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China. · Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Social Circuits behind Multi-agent Echo Chambers
Sep 28, 2026Chuiyang Meng, Wenlu Yu, Ming Tang +1Multi-Agent LLM Systems Multi-Agent Systems
Simon Fraser University · University of Alberta · Southern University of Science and Technology
Waggle: Learning One Anonymous Local Law for Self-Organizing LLM Swarms
Sep 28, 2026Mingxi Zou, Wei Zhu, Zhuo Wang +4Multi-Agent LLM Systems Multi-Agent Coordination
Shanghai Academy of AI for Science (SAIS) · AI3 Institute, Fudan University · Yunnan University
KVCMAS: Efficient KV cache Correction for Shared Context in Multi-Agent Systems
Sep 28, 2026Hyesung Jeon, Hyeongju Ha, Seoyoung Lee +2Multi-Agent LLM Systems KV-Cache Management
Department of Electrical and Computer Engineering Seoul National University
PReCache: Efficient KV Cache Sharing for Multi-LoRA Agents via Low-Rank Precomputation and Neutral Reconstruction
Sep 28, 2026Hyesung Jeon, Hyeongju Ha, Jae-Joon KimMulti-Agent LLM Systems Memory-Efficient Inference
Department of Electrical and Computer Engineering Seoul National University
Large Language Models for Structured Clinical Data Analysis: Dual-Agent Grounding and Validation
Sep 28, 2026Erfan D. Dehkalani, Seetha Shankaran, Abbot R. Laptook +3Multi-Agent LLM Systems LLM Grounding
Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, and Harvard Medical School, Boston, MA, USA · Department of Pediatrics, Wayne State University School of Medicine, Detroit, MI, USA · Department of Pediatrics, Women & Infants Hospital of Rhode Island, and Warren Alpert Medical School of Brown University, Providence, RI, USA +1
Maat: Independent Deterministic Contract-Based Governance for Multi-Agent LLM Workflows
Sep 27, 2026Uliana ElinaMulti-Agent LLM Systems LLM Agent Verification
SynWe Group s.r.o.
Prospective Interpretation Risk: Principled Communication Control Between LLMs
Sep 27, 2026Wanrong Yang, Rehan Deen, Julian Ma +7Multi-Agent LLM Systems Pragmatic Reasoning in Language Models
University of Liverpool · Independent Researcher · University College London +1
Population Physics, Population Problems: Safety and Emergence in LLM Societies
Sep 27, 2026Adrian de WynterMulti-Agent LLM Systems Large Language Model-Based Social Simulation
Microsoft & The University of York
DEALS: Decentralized Expertise-Aware Load Serving for Multi-Agent LLM Systems
Sep 27, 2026Jingjuan Huang, Wenbin Wang, Yanchuan Yin +2Multi-Agent LLM Systems Multi-Agent Coordination
Ohio State University · University of Colorado at Boulder
RepoMAS: Solving Progressively Specified Tasks with Issue-Driven Multi-Agent Systems
Sep 26, 2026Yuchen Song, Andong Chen, Wenxin Zhu +2Multi-Agent LLM Systems Multi-Agent Orchestration
Harbin Institute of Technology, Harbin, China
Prefill-Free Cross-Family KV Cache Transfer for Heterogeneous Multi-Agent LLMs
Sep 26, 2026Vincent-Daniel Yun, Woosang Lim, Haneul Yoo +3Multi-Agent LLM Systems LLM Inference Acceleration
University of Southern California, USA · Seoul National University, Republic of Korea · New York University, USA
How does Adversarial Influence Scale in Multi-Agent Systems?
Sep 24, 2026Addison J. Wu, Jasin Cekinmez, Michel Liao +2Multi-Agent LLM Systems Deception in Language Models
Princeton University
Epistemic-Probabilistic Model for Guarded Multi-Agent LLM Coordination
Sep 24, 2026Mehdi Nasiri, Mohammad Saeed Arvenaghi, Sadegh Vaezi +1Multi-Agent LLM Systems Multi-Agent Coordination
Pasargad Institute for Advanced Innovative Solutions · Iran University of Science and Technology
AlphaDiverse: Post-Training Local Quantitative Research Agents for Diverse Exploration in Alpha Factor Mining
Sep 24, 2026Qingzhuo Wang, Zikun Wei, Zhihua Wei +1Multi-Agent LLM Systems Quantitative Finance
Tongji University · Shanghai Non-convex Intelligent Technology
MeshHeal: Two-Timescale Self-Healing for Gray Failures in Decentralized LLM Agent Networks
Sep 24, 2026Keru Chen, Sen Lin, Yingbin Liang +2Multi-Agent LLM Systems Multi-Agent Systems
School of ECEE Arizona State University · Computer Science Department University of Houston · Department of ECE The Ohio State University +1
Codetta: High-Capacity, Keyless, and Undetectable Multi-Agent Collusion
Sep 24, 2026Qi Pang, Virginia Smith, Wenting ZhengMulti-Agent LLM Systems Algorithmic Collusion
Carnegie Mellon University
Agent Memory with Episodic Retrieval for Financial Decision-Making
Sep 23, 2026Nuoyue Xu, Jiang Liu, Wenxuan Huang +3Multi-Agent LLM Systems LLM Agent Memory
University of British Columbia · University of Science and Technology of China · Fudan University +1
MAGIC: Mixed-Granularity Agent Graphs via Incremental Construction with Dense-Reward Reinforcement Learning
Sep 22, 2026Kairui Yang, Ziheng Yi, Xunkai Li +4Multi-Agent LLM Systems Multi-Agent Systems

Sep 29, 2026, cs.CRJ/K move · Enter open · S save

Concealing LLM-Based Multi-Agent Topology via Phantom Structure Injection

Longzhu He, Zelang Wen, Xinfeng Li, Sen Su+1

Nanyang Technological University · Beijing University of Posts and Telecommunications · The Hong Kong Polytechnic University

Multi-Agent LLM Systems Multi-Agent System Security

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Driven by the rapid advancement of large language models (LLMs), LLM-based multi-agent systems (MAS) have emerged as a powerful paradigm for collaborative reasoning over complex tasks. A key design element of MAS is the communication topology, which governs information flow among agents and often encodes proprietary knowledge about the system architecture. However, recent work has shown that such topologies can be inferred even in black-box settings by exploiting semantic dependencies in observable reasoning traces, posing significant risks of intellectual property leakage and exposure of system vulnerabilities. To address this threat, we propose MIRAGE, a topology-concealment framework that preserves the genuine communication topology for task execution while shaping adversary-facing semantic evidence toward a carefully constructed phantom topology. Specifically, MIRAGE operates in three stages: (1) phantom topology synthesis, (2) semantic edge realization, and (3) protected MAS execution. It constructs a phantom topology structurally distinct from the genuine one, materializes phantom edges as plausible semantic dependencies, and suppresses source-specific cues that could reveal genuine edges absent from the phantom topology. Extensive experiments across three topology optimization frameworks and four benchmark datasets demonstrate that MIRAGE substantially reduces the effectiveness of topology inference attacks while largely preserving the task utility of the protected MAS.

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Latest papers 665