PE-MA: Parameter-Efficient Co-Evolution of Multi-Agent Systems

--Multi-Agent Systems have recently emerged as a promising paradigm for collaborative reasoning and solving complex tasks. However, the design of collaborative learning algorithms in multi-agent systems faces several challenges, including high communication overhead and insufficient agent-level person-alization. In this paper, we propose PE-MA (Parameter-Efficient Multi-Agent Co-Evolution), a novel collaboration framework that supports efficient, scalable, and personalized co-evolution in multi-agent systems. In PE-MA, each agent maintains a lightweight personalized adapter to support agent-specific behavior, while a shared adapter is collaboratively optimized across neighboring agents. Experiments show that PE-MA improves accuracy by 2%-5%, while reducing training and communication costs by 77% and 87%, respectively. N recent years, multi-agent systems (MAS) have gradually become an important research topic in the field of artificial intelligence due to breakthroughs in natural language understanding and generation. These systems organize multiple agents with communication capabilities into collaborative frameworks, enhancing their abilities in task decomposition, role allocation, and collective reasoning. This drives the paradigm of solving complex tasks toward greater intelligence and autonomy. It has been widely applied in intelligent dialogue systems [1; 2], automated decision-making [3], robotic collaboration [4; 5], and virtual assistants [6; 7], demonstrating significant application potential.