Multi-Agent Systems

Comprehensive Survey on the Development and Application of AI Agents in 2023: Concepts, Principles, Development, Applications, Challenges, and Prospects

by
Comprehensive Survey on the Development and Application of AI Agents in 2023: Concepts, Principles, Development, Applications, Challenges, and Prospects

The field of Artificial Intelligence (AI) is rapidly evolving.Today’s AI agents are capable of perceiving, making decisions, and taking actions independently. With the rise of AI agents driven by large language models (LLMs), we are on the brink of a new era: AI agents may form their own societies and coexist harmoniously with humans. Newton … Read more

Multi-Agent AI Systems: The Future of Intelligent Transformation in Enterprises

by
Multi-Agent AI Systems: The Future of Intelligent Transformation in Enterprises

In today’s digital age, artificial intelligence (AI) is no longer just a tool; it is evolving into an intelligent workforce composed of numerous AI agents capable of autonomous planning, reasoning, and task execution. The rise of Multi-Agent Systems (MAS) is fundamentally changing the way businesses operate by enabling specialized AI agents to collaborate seamlessly, tackling … Read more

AGDebugger: A Powerful Tool for Developing, Debugging, and Guiding Multi-Agent Systems

by
AGDebugger: A Powerful Tool for Developing, Debugging, and Guiding Multi-Agent Systems

Click 👇🏻 to follow, the article comes from 🙋♂️ Friends who want to join the community can see the method at the end of the article for group communication. “ In the current rapid development of AI technology, multi-agent AI systems are gradually becoming a popular choice for solving complex tasks. However, this also brings … Read more

Why Multi-Agent Systems Fail? Two Interesting Experimental Conclusions on R1 Class Reasoning Model Training and Inference

by
Why Multi-Agent Systems Fail? Two Interesting Experimental Conclusions on R1 Class Reasoning Model Training and Inference

Today is March 27, 2025, Thursday, Beijing, clear weather. Today, we continue to discuss the R1 reasoning model and the topic of multi-agents. There are three interesting experimental reports. They are: Enhancing LLM Reasoning by Scaling Multi-round Test-time Thinking (Think Twice), Length of Training Data is More Important than Difficulty for Training Reasoning Models, and … Read more

Research on Continuous Control of Safety-Critical Multi-Agent Systems Based on Quadratic Programming under Uncertainty

by
Research on Continuous Control of Safety-Critical Multi-Agent Systems Based on Quadratic Programming under Uncertainty

[Image]Gift to Readers[Image] Conducting research involves a profound system of thought, requiring researchers to be logical, meticulous, and diligent. However, mere effort is not enough; often leveraging resources is more important than hard work. Additionally, one must have innovative ideas and inspirations that look up to the stars. It is recommended that readers browse through … Read more

Why Do Multi-Agent Systems Fail in Task Execution?

by
Why Do Multi-Agent Systems Fail in Task Execution?

Click the blue text to follow us Recently, scholars from the University of California, Berkeley published a paper titled “Why Do Multi-Agent LLM Systems Fail?”, analyzing and summarizing the failure modes of multi-agent systems. I have tried several popular multi-agent development frameworks, and while the demo examples can run successfully, there is still a distance … Read more

Why Do Multi-Agent LLM Systems Fail?

by
Why Do Multi-Agent LLM Systems Fail?

Multi-Agent Large Language Model (LLM) systems can fail? Recently, the University of California, Berkeley published a significant paper titled “Why Do Multi-Agent LLM Systems Fail?” which delves into the reasons for the failures of MAS systems, outlining 14 specific failure modes and providing corresponding improvement suggestions. Below is the translation of the paper, Enjoy. Introduction … Read more

The Challenges of Multi-Agent Systems: Insights from UC Berkeley Research

by
The Challenges of Multi-Agent Systems: Insights from UC Berkeley Research

In the past two years, the most exciting development in the field of AI has been the rise of large language models (LLMs), which have demonstrated remarkable understanding and generation capabilities. Building on this foundation, a grander vision has emerged: to construct Multi-Agent Systems (MAS). Imagine not a single AI working alone, but a “dream … Read more

Low Success Rate of Multi-Agent Systems? An In-Depth Analysis of 14 Failure Modes and the Root Causes Hidden in These 3 Key Stages!

by
Low Success Rate of Multi-Agent Systems? An In-Depth Analysis of 14 Failure Modes and the Root Causes Hidden in These 3 Key Stages!

🔥 [Heartbreaking Data at the Beginning]Research shows that even top open-source multi-agent systems like ChatDev have a task accuracy rate of only 25%! Why does the theoretical “collaborative intelligence” frequently fail? We reveal the truth behind the failures using over 150 dialogue trajectories and expert annotations! The Gap Between the “Ideal and Reality” of Multi-Agent … Read more

Why Do Multi-Agent Systems Often Fail? Berkeley Points Out Their Human Collaboration Flaws!

by
Why Do Multi-Agent Systems Often Fail? Berkeley Points Out Their Human Collaboration Flaws!

Paper: Why Do Multi-Agent LLM Systems Fail? Link: https://arxiv.org/pdf/2503.13657 Why Do Multi-Agent Systems Fail? Imagine you have assembled a team: programmers, testers, and project managers each performing their roles. However, the delivered product is riddled with bugs, team members blame each other, and some even alter requirements without permission—this is not a workplace drama, but … Read more