Multi-Agent Collaborative Reasoning via Cloud-Edge Framework

Large Language Model (LLM)-based agents have significantly advanced the reasoning and problem-solving capabilities of intelligent systems. As tasks grow more complex, there is an increasing need to shift from single-agent framework to Multi-Agent Systems (MAS) for enhanced collaborative performance. Achieving better performance typically requires deploying large-scale LLMs, however, it poses significant challenges on resource-constrained edge devices. In addition, most MAS adopt a fully connected communication topology and require multiple rounds of interaction, resulting in considerable communication redundancy and increased computational overhead. To address the issues, in this paper, we propose a Directed Acyclic Graph-based Multi-Agent System (DAG-MAS) via cloud-edge framework for collaborative reasoning. Specifically, considering the limited resources on the edge, we utilize a 72B-LLM in the cloud finetunes a lightweight Small Language Model (SLM) for edge deployment. To generate a one round interaction graph, the SLM is further employed to analyze task requirements and determine optimal communication topology, minimizing overhead while enabling effective information exchange. Extensive experiments and analyses on four reasoning datasets demonstrate the superiority of our approach compared to several benchmark methods, validating the effectiveness in achieving higher reasoning performance with lower communication costs.