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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CANTANTE: Optimizing Agentic Systems via Contrastive Credit Attribution
May 13, 2026Tom ZehleMulti-Agent LLM Systems Multi-Agent System Optimization
University of Freiburg · ELLIS Institute Tübingen
A Multi-Agent Orchestration Framework for Venture Capital Due Diligence
May 13, 2026Grigorios Alexandrou, Katerina PramatariMulti-Agent LLM Systems LLM Agent Orchestration
Department of Management Science and Technology, Athens University ofMay Economics and Business (AUEB), Athens, Greece
Not Just RLHF: Why Alignment Alone Won't Fix Multi-Agent Sycophancy
May 13, 2026Adarsh Kumarappan, Ananya MujooAI Agent Reliability Multi-Agent LLM Systems
1California Institute of Technology · 2Evergreen Valley College
Reinforced Collaboration in Multi-Agent Flow Networks
May 13, 2026Zheng Wang, Yuang Liu, Yangkai DingMulti-Agent LLM Systems Multi-Agent Collaboration
Huawei Technologies Co., Ltd.
CHAL: Council of Hierarchical Agentic Language
May 12, 2026Tommaso Giovannelli, Griffin D. KentBelief Revision Multi-Agent LLM Systems
ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows
May 12, 2026Wei Liu, Yang Gu, Xi Yan +5Multi-Agent LLM Systems Agentic Workflows
School of Software & Microelectronics, Peking University Beijing, China · Center for Machine Learning Research, Peking University Beijing, China Zhongguancun Academy Beijing, China
Iterative Audit Convergence in LLM-Managed Multi-Agent Systems: A Case Study in Prompt-Engineering Quality Assurance
May 12, 2026Elias CalboreanuMulti-Agent LLM Systems AI Agent Auditing
Swift AI Lab, The Swift Group, LLC, Reston, VA 20190, USA
No Action Without a NOD: A Heterogeneous Multi-Agent Architecture for Reliable Service Agents
May 12, 2026Zixu Yang, Hang Zheng, Nan Jiang +5AI Agent Reliability Multi-Agent LLM Systems
1X-LANCE Lab, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China · 2Shanghai Innovation Institution, Shanghai, China · 3Jiangsu Key Lab of Language Computing, Suzhou, China
Attributing Emergence in Million-Agent Systems
May 12, 2026Ling Tang, Jilin Mei, Qian Chen +6Multi-Agent LLM Systems Computational Social Science
Shanghai Artificial Intelligence Laboratory · Fudan University · Tongji University +1
LLM-X: A Scalable Negotiation-Oriented Exchange for Communication Among Personal LLM Agents
May 12, 2026Giuliano Lorenzoni, Paulo Alencar, Donald CowanMulti-Agent LLM Systems Multi-Agent Coordination
University of Waterloo · University of Waterloo Waterloo, ON, Canada
Strategic Coercion Within Alliances: The Greenland Sovereignty Game as an AI Stress Test
May 11, 2026Rommin Adl, Peyton WilliamsMulti-Agent LLM Systems AI Agent Benchmarks
Grinnell College
EVOCHAMBER: Test-Time Co-evolution of Multi-Agent System at Individual, Team, and Population Scales
May 11, 2026Yaolun Zhang, Tianyi Xu, Shengyu Dai +3Multi-Agent LLM Systems Multi-Agent Collaboration
1Oregon State University · 5AG2AI, Inc. · University of Wisconsin–Madison +2
NanoResearch: Co-Evolving Skills, Memory, and Policy for Personalized Research Automation
May 11, 2026Jinhang Xu, Qiyuan Zhu, Yujun Wu +11Multi-Agent LLM Systems LLM Agent Skill Learning
1Shanghai Artificial Intelligence Laboratory · 2The Hong Kong University of Science and Technology · 3Peking University +4
PRISM: Generation-Time Detection and Mitigation of Secret Leakage in Multi-Agent LLM Pipelines
May 11, 2026Riya Tapwal, Abhishek Kumar, Carsten MapleData Leakage Multi-Agent LLM Systems
School of Computing and Electrical Engineering · Indian Institute of Technology, Mandi (IIT) · The Alan Turing Institute, London +1
Collective Alignment in LLM Multi-Agent Systems: Disentangling Bias from Cooperation via Statistical Physics
May 11, 2026Cristiano De NobiliMulti-Agent LLM Systems Multi-Agent Coordination
Critiqality, Milan, Italy
Safe Multi-Agent Behavior Must Be Maintained, Not Merely Asserted: Constraint Drift in LLM-Based Multi-Agent Systems
May 11, 2026Tianxiao Li, Yixing Ma, Haiquan Wen +4Multi-Agent LLM Systems AI Agent Governance
University of Liverpool, UK · University of Nottingham, UK · University of Tokyo, Japan +1
TMAS: Scaling Test-Time Compute via Multi-Agent Synergy
May 11, 2026George Wu, Nan Jing, Qing Yi +7Multi-Agent LLM Systems RL for Language Model Reasoning
1IQuest Research · 2Beihang University
AgentRx: A Benchmark Study of LLM Agents for Multimodal Clinical Prediction Tasks
May 11, 2026Baraa Al Jorf, Farah E. ShamoutMulti-Agent LLM Systems LLM Agent Evaluation
New York University Abu Dhabi, UAE
NyayaAI: An AI-Powered Legal Assistant Using Multi-Agent Architecture and Retrieval-Augmented Generation
May 11, 2026Deepanshu, Divi Saxena, Deepali Rana +2Multi-Agent LLM Systems Legal IR
School of Computer Science UPES, Dehradun, India
MAGE: Multi-Agent Self-Evolution with Co-Evolutionary Knowledge Graphs
May 11, 2026Ruiyi Yang, Zechen Li, Hao Xue +2Agent Memory Continual Learning for LLM Agents
University of New South Wales · The Hong Kong University of Science and Technology (Guangzhou) · Mohamed Bin Zayed University of Artificial Intelligence
STAR: Failure-Aware Markovian Routing for Multi-Agent Spatiotemporal Reasoning
May 11, 2026Ruiyi Yang, Lihuan Li, Hao Xue +1Multi-Agent LLM Systems Spatiotemporal Reasoning
University of New South Wales · The Hong Kong University of Science and Technology (Guangzhou)
RADAR: Redundancy-Aware Diffusion for Multi-Agent Communication Structure Generation
May 11, 2026Zhen Zhang, Wanjing Zhou, Juncheng Li +3Multi-Agent LLM Systems Multi-Agent System Optimization
National Key Laboratory for Novel Software Technology, Nanjing University, China · Zhejiang University · University of Oxford +1
CalBench: Evaluating Coordination-Privacy Trade-offs in Multi-Agent LLMs
May 10, 2026Chelsea Zou, Yiheng Yao, Selena She +2Multi-Agent LLM Systems Multi-Agent Coordination
Stanford University
UTS at PsyDefDetect: Multi-Agent Councils and Absence-Based Reasoning for Defense Mechanism Classification
May 10, 2026Dima Galat, Marian-Andrei RizoiuMulti-Agent LLM Systems Mental Health
University of Technology Sydney
Workspace Optimization: How to Train Your Agent
May 10, 2026Elad Sarafian, Gal Kaplun, Ron Banner +2Multi-Agent LLM Systems LLM Agent Self-Improvement
NVIDIA · NVIDIA & Technion
TacoMAS: Test-Time Co-Evolution of Topology and Capability in LLM-based Multi-Agent Systems
May 10, 2026Chen Xu, Yicheng Hu, Ruizi Wang +4Multi-Agent LLM Systems Multi-Agent System Optimization
1Carnegie Mellon University · University of Science and Technology of China · 3National University of Singapore +1
Robust Multi-Agent LLMs under Byzantine Faults
May 9, 2026Haejoon Lee, Vincent-Daniel Yun, Dimitra Panagou +1Multi-Agent LLM Systems Multi-Agent Coordination
Department of Robotics, University of Michigan, Ann Arbor, USA · Thomas Lord Department of Computer Science, University of Southern California, USA
Trustworthy AI: Ensuring Reliability and Accountability from Models to Agents
May 9, 2026Carol Xuan LongMulti-Agent LLM Systems AI Accountability
John A. Paulson School Of Engineering And Applied Sciences, Harvard University, Cambridge, Massachusetts
AgentSlimming: Towards Efficient and Cost-Aware Multi-Agent Systems
May 9, 2026Yulang Chen, Haoxuan Peng, Jinyan Liu +3Multi-Agent LLM Systems Multi-Agent System Optimization
1Shanghai Jiao Tong University · 2Nanjing University · 3The University of Sydney +2
EvoMAS: Learning Execution-Time Workflows for Multi-Agent Systems
May 9, 2026Chengdong Xu, Kaiqiang Ke, Ziheng Liu +4Multi-Agent LLM Systems Multi-Agent System Optimization
Sun Yat-Sen University · ShanghaiTech University · Zhejiang University +1

May 13, 2026, cs.CLJ/K move · Enter open · S save

CANTANTE: Optimizing Agentic Systems via Contrastive Credit Attribution

Tom Zehle

University of Freiburg · ELLIS Institute Tübingen

Multi-Agent LLM Systems Multi-Agent System Optimization Large Language Model-Guided Optimization LLM Agent Workflow Optimization Prompt Optimization Multi-Agent Reinforcement Learning

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LLM-based multi-agent systems have demonstrated strong performance across complex real-world tasks, such as software engineering, predictive modeling, and retrieval-augmented generation. Yet automating their configuration remains a structural challenge, as scores are available only at the system level, whereas the parameters governing agent behavior are local. We argue that optimizing these systems is fundamentally a credit-assignment problem. We therefore introduce CANTANTE, a framework that decomposes system-level rewards into per-agent update signals by contrasting rollouts of multiple joint configurations on the same query. We instantiate it for prompt optimization, treating agent prompts as learnable system parameters. We evaluate CANTANTE against GEPA and MIPROv2 on programming (MBPP), mathematical reasoning (GSM8K), and multi-hop question answering (HotpotQA). Across these benchmarks, CANTANTE achieves the best average rank among all evaluated optimizers and consistently outperforms unoptimized prompts. It improves over the strongest baseline by +18.9 percentage points on MBPP and +12.5 percentage points on GSM8K, while incurring a lower inference cost. It remains within one standard deviation of the strongest baseline on HotpotQA. Crucially, our credit correlation analysis confirms that the attributer produces meaningful per-agent signals rather than echoing the global system score.

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Latest papers 665