arXiv:2602.04234v6 Announce Type: cross Abstract: Multi-agent systems (MAS) have emerged as a prominent paradigm for leveraging large language models (LLMs) to tackle complex tasks. However, the mechanisms governing the effectiveness of MAS built upon publicly available LLMs, specifically the underlying rationales for their success or failure, remain largely unexplored. In this paper, we revisit MAS through the perspective of \textit{entropy}, considering both intra- and inter-agent dynamics by investigating entropy transitions during problem-solving across various topologies, six reasoning be
This research is emerging as multi-agent systems, particularly with large language models, are becoming a key focus for developing more capable AI. Understanding their internal dynamics is crucial for advancing the field beyond empirical approaches.
A strategic reader should care because deeper understanding of multi-agent collaboration mechanisms directly impacts the design, performance, and reliability of advanced AI systems. This work could accelerate or constrain the development of scalable autonomous AI.
The ability to formally model and predict the effectiveness of multi-agent systems using entropy offers a more principled approach to their development, moving beyond trial-and-error. This could lead to more robust and explainable AI agents.
- · AI researchers focusing on theoretical foundations
- · Developers of multi-agent AI platforms
- · Industries deploying complex autonomous systems
- · Companies relying solely on empirical, black-box multi-agent AI development
Increased efficiency in designing and optimizing multi-agent large language model systems.
Faster development and deployment of complex AI agents capable of tackling previously intractable problems.
Enhanced trust and adoption of autonomous AI in critical applications due to more predictable and explainable behavior.
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Read at arXiv cs.AI