Multi-Agent LLM Systems

LLM: Large Language Model

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AaLLM: An End-to-End Analog Circuit Design Framework from Topology Generation to Sizing Using Large Language Models
Aug 13, 2026Mohammed Ayman Habib, Rylan Hart, Morteza FayaziMulti-Agent LLM Systems Electronic Design Automation
University of Utah, Salt Lake City, Utah, USA
StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems
Aug 13, 2026Yanwen Peng, Delvin Ce Zhang, Xi Wang +1Multi-Agent LLM Systems Multi-Agent Communication
School of Computer Science, University of Sheffield Sheffield, United Kingdom
Discovering Efficient and Explainable Communication Topologies for LLM-based Multi-Agent Systems via Causal Inference
Aug 13, 2026Junzhi Li, Peng He, Qirui Ji +3Multi-Agent LLM Systems Causal Attribution
University of Chinese Academy of Sciences · National Key Laboratory of Space Integrated Information System, Institute of Software, Chinese Academy of Sciences · Beijing University of Posts and Telecommunications
Lines and Ladders: A Context-Aware Multi-Agent Framework for Large-Scale Retail Price Taxonomy
Aug 13, 2026Ravi Teja Chunduri, Srikaran Reddy Boya, Deep Narayan Mishra +3Multi-Agent LLM Systems
Walmart Global Tech Bentonville, AR, USA
One Frozen Simulator Is Not Enough: Simulator Collapse in Multi-Agent RL
Aug 12, 2026Simon Yu, Nicholas Tomlin, Marwa Abdulhai +7Multi-Agent LLM Systems Human Behavior Simulation
1Northeastern University · 2New York University · 3UC Berkeley +2
GUIDE: Governed Unified Intelligence for Document-to-Artifact Generation in Enterprise Settings
Aug 12, 2026Shivali Dalmia, Sumukha Thoppanahalli, Mohammadreza Sediqin +1Multi-Agent LLM Systems Document Information Extraction
Centific Research Washington, USA
Do LLMs Take Care of Their Own? Similarity Signals Can Induce Cooperation
Aug 12, 2026Akash Kundu, Emanuel Tewolde, Ratip Emin Berker +2Multi-Agent LLM Systems LLM Agent Evaluation
Cooperative AI Research Fellowship · Carnegie Mellon University · Foundations of Cooperative AI Lab (FOCAL)
Advancing MLLM-based UAV Image Understanding and Reasoning: A Benchmark and a Training-Free Multi-Agent System
Aug 12, 2026Haoyu Zhang, Shuoxun Zhang, Peng Ye +5Visual Question Answering Multi-Agent LLM Systems
Embedded Deep Learning and Visual Analysis Laboratory, College of Future Information Technology, Fudan University, Shanghai 200433, China · Embedded Deep Learning and Visual Analysis Laboratory, College of Future Information Technology, Fudan University, Shanghai 200433, China, and also with Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China, and also with The Chinese University of Hong Kong, Hong Kong · College of Future Information Technology, Fudan University, Shanghai 200433, China +1
From Numbers to Judgment: Specialist LLM Agents and Reinforcement Learning for European Listed Real Estate
Aug 11, 2026Pardis Taghavi, Santosh BhavaniNumerical Reasoning in Language Models Multi-Agent LLM Systems
Santosh Bhavani∗
ASCon: A Direction-Aware Reciprocal Agent--Step Contextualization Model for Failure Attribution in Multi-Agent Systems
Aug 11, 2026Shuyu Jiang, Yue Ran, Kaiyu Xu +5Agent Failure Analysis Multi-Agent LLM Systems
1Sichuan University, China · 2Nanyang Technological University, Singapore
Mind Viruses: Self-Propagating Ideas in Multi-Agent LLM Systems
Aug 10, 2026Vassilis Papadopoulos, McNair Shah, Sam Zimmerman +1Multi-Agent LLM Systems Multi-Agent Systems
1Anthropic Fellows Program · 2EPFL · 3Anthropic
Beyond Cash Flows: A Multi-Agent AI Framework for Valuing Clinical-Stage, Cross-Border Biotechnology
Aug 10, 2026Yuhan FangMulti-Agent LLM Systems Multi-Agent Coordination
CPC Scientific Inc.
The Politician, the Liar, and the Obedient Worker: Emerging Behavior of LLM Agents in Hierarchical Games
Aug 10, 2026Fatemeh Seyedin, Adrian Weller, Jinhyuk Yun +1Multi-Agent LLM Systems Multi-Agent Coordination
Berlin, Germany · Cambridge, UK · Soongsil University +1
Coupled Graph--Policy Distillation for Personalized Medication Safety in Older Adults with Multimorbidity
Aug 10, 2026Zihan Wang, Anglin Liu, Rongyi Wang +6Multi-Agent LLM Systems Healthcare
1Artificial Intelligence Thrust, The Hong Kong University of Science and Technology (Guangzhou) · Faculty of Engineering, University of New South Wales · 3Medical Big Data Center, Guangdong Provincial People’s Hospital, Southern Medical University +2
MoRSE: Task-Oriented Multi-Agent System with Mixture of Role-Subtask Experts
Aug 10, 2026Peiwen Li, Shiyang Zhang, Yangtian Zhang +3Multi-Agent LLM Systems Mixture-of-Experts Language Models
Yale University, New Haven, CT, USA
ForestBench: A Unified Graph Framework for Evaluating Multi-Agent Collaboration
Aug 9, 2026Guo Chen, Ziwen Li, Reed Li +4Multi-Agent LLM Systems AI Agent Evaluation
Southwest University Chongqing, China · Tencent Beijing, China · Institute of Computing Technology, Chinese Academy of Sciences Beijing, China
SocialFiVis: A Visual Analytics Sandbox for LLM-Grounded Multi-Agent Simulation in Social Finance
Aug 9, 2026Yi-Fan Cao, Qing Shi, Liangwei Wang +5Multi-Agent LLM Systems Human Behavior Simulation
The Hong Kong University of Science and Technology (HKUST) · The University of Hong Kong (HKU)
LatticeMind: A Conflict-Aware Memory Primitive for Multi-Agent Systems
Aug 8, 2026Heng Zhou, Lian Zhang, Yutao Fan +5Multi-Agent LLM Systems Knowledge Conflicts in Language Models
University of Science and Technology of China · University of Illinois Urbana-Champaign · Shanghai AI Laboratory +2
Persuasive and Compliant Tendencies Predict Group Decision-Making in Humans and Language Models
Aug 8, 2026Wenwen He, Wenke Huang, Wei Yang Bryan Lim +1Multi-Agent LLM Systems Personality Modeling in Language Models
College of Computing and Data Science, Nanyang Technological University, Singapore
Verication-driven closed-loop multi-agent large language modelframework for code-compliant structural design
Aug 8, 2026Jianbin Luo, Weibin Lin, Yiran Lin +2Multi-Agent LLM Systems LLM Agent Reliability
College of Civil Engineering, Fuzhou University, Fuzhou, 350108, China
A Two-Tier Perspective on Inference-Time Parallelism in Multi-Agent LLM Systems
Aug 6, 2026Zihan Xu, Haolin Tian, Hai JiangLLM Inference Efficiency Multi-Agent LLM Systems
Beijing University of Posts and Telecommunications.
Adaptive Arena-based Contestable Argumentative Network-of-Experts for Open-Ended Care Plan Coordination
Aug 5, 2026Truong Thanh Hung Nguyen, Hoang-Loc Cao, Phuc Ho +3Multi-Agent LLM Systems Healthcare
Analytics Everywhere Lab, University of New Brunswick, Canada · National Research Council Canada, Canada
ArtAnno: Annotating Implicit Semantics in Artworks through LLM Agent-Driven Bidirectional Human-AI Augmentation
Aug 5, 2026Xiaoyan Gu, Yifang Wang, Wenqing Zheng +6Multi-Agent LLM Systems LLM-Assisted Annotation
School of Computer and Computing Science, Hangzhou City University Hangzhou, Zhejiang, China
A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination
Aug 5, 2026Wenxiao Zhao, Dong Liu, Kaiyi Xu +9Multi-Agent LLM Systems LLM Agent Training
Shanghai AI Laboratory · University of California at Los Angeles · Tongji University
HELENA:Hierarchical Sparse Coordination over a Union of Complementary Topologies for MAS
Aug 5, 2026Zhifang Mao, Linyao Zheng, Xuhang Shi +1Multi-Agent LLM Systems Multi-Agent Coordination
1XiaoLab · 2Xi’an Jiaotong University · 3Beijing University of Posts and Telecommunications
RouteGuard: Certifying Routing Gain in LLM Multi-Agent Systems When Complementarity Is Not Enough
Aug 5, 2026Anchen Sun, Kaiqi YangMulti-Agent LLM Systems AI Agent Benchmarks
Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33146 USA · Department of Mathematics, University of Miami, Coral Gables, FL 33146 USA
Continuous Improvement and Parallel Autonomous Exploration: An LLM-Agent Framework for Searching Large Solution Spaces
Aug 5, 2026Dulmini Hettiarachchi, Andre Rusli, Julio Christian Young +1Multi-Agent LLM Systems Self-Improving Agents
Mercari, Inc. Japan
CURATE: Leveraging LLM Agents to Compose, Catalog, and Deploy Reproducible Workflows
Aug 4, 2026Nolan Cutler, Chia-Chen Kuo, Nanda Velugoti +2Multi-Agent LLM Systems Human-in-the-Loop AI
School of EECS Oregon State University · School of CBEE Oregon State University
Agents Catching Agents: Shortcut Cascades and Benchmark Gaming in Clinical Multi-Agent Systems
Aug 4, 2026Sebastián Andrés Cajas Ordóñez, Agastya Munnangi, Aldo Marzullo +13AI Agent Reliability Multi-Agent LLM Systems
MIT Critical Data, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States · Georgia State University, Atlanta, Georgia, United States · Politecnico di Milano, Milan, Italy +12
AgentPanel: Toward a New Paradigm for Human--AI Collaboration in Exploring Scientific Questions
Aug 4, 2026Zhiyao Cui, Qianyi Wang, Haoyang Yan +26Multi-Agent LLM Systems Scientific Hypothesis Generation
Shanghai Artificial Intelligence Laboratory

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AaLLM: An End-to-End Analog Circuit Design Framework from Topology Generation to Sizing Using Large Language Models

Mohammed Ayman Habib, Rylan Hart, Morteza Fayazi

University of Utah, Salt Lake City, Utah, USA

Multi-Agent LLM Systems Electronic Design Automation

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Analog circuit design is a time-consuming, iterative process in a nonlinear and high-dimensional design space that relies heavily on expert intuition. Among recent developments, LLMs have introduced a promising approach by bringing natural language reasoning to circuit design tasks. The majority of conventional LLM-based approaches provide fragmented solutions that focus either only on sizing or topology generation. These methods require adding specific technical knowledge manually, which is inefficient and prone to hallucinations during circuit sizing. Moreover, the inherent trade-off in meeting different specs makes current approaches iterative and tedious. Another shortcoming is the inability to create innovative topologies, which may lead to sub-optimal designs due to reliance on conventional topologies. In this paper, we present AaLLM, an open-source end-to-end multi-agent LLM workflow that takes user specs as input and outputs the appropriate netlist, encompassing both topology generation and circuit sizing. AaLLM automates the creation of a relevant knowledge base from research papers and textbooks to combat tedious manual data collection. A RAG model is implemented to emulate circuit design expertise using this knowledge base. Moreover, AaLLM uses a novel tri-agent feedback system comprising a Designer that determines circuit component values, a Critic that scrutinizes these values, and an Evaluator that minimizes circuit sizing iterations by arbitrating between the other two agents. AaLLM-generated novel topologies achieve a figure of merit (FoM) comparable to that of known topologies, and up to 3x higher for certain circuits. Testing on several circuit topologies, our results show a 3x - 4.5x decrease in the number of SPICE calls at inference when compared to SOTA multi-agent LLM pipelines. The results also show a 40x decrease in wall-clock time compared to existing approaches.

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