Adaptive Organization-Centric Collaboration for LLM-Based Multi-Agent Systems

Large language model (LLM)-based multi-agent systems (MAS) excel at complex tasks, and their effectiveness depends critically on how agents are organized to collaborate. However, most existing methods design collaboration only at the instance level, generating for each query a concrete topology that fixes which agents participate and how they communicate before execution. They leave unmodeled the organizational level of collaboration, which comprises the groups through which agents collaborate, how these groups form and change as the task unfolds, and how context passes between them. As a result, these methods cannot adapt collaboration during execution, and the organizational knowledge they learn stays tied to whole-task topologies. Inspired by the Agent-Group-Role (AGR) organizational model, which separates the organizational structure from the concrete organizations that instantiate it, we lift collaboration design to the organizational level and propose AGR-AR, an organization-centric framework. AGR-AR learns how to organize agents and instantiates groups on demand during execution. At each step, a lightweight autoregressive topology generator constructs the next group conditioned on the task and execution feedback, selecting its members, including overlapping agents that carry context from earlier groups, and determining how they communicate. The organization thus adapts during execution, and a single generator serves tasks across domains. Experiments on six benchmarks show that AGR-AR achieves higher average accuracy than strong baselines while consuming substantially fewer tokens and remaining more robust to adversarial agents; without retraining, it also incorporates unseen agents and transfers to unseen tasks. Code is available at https://anonymous.4open.science/r/AGR-AR-ECF6/.