As large language model (LLM)-based multi-agent systems scale to handle increasingly complex tasks, balancing structural stability and dynamic adaptability becomes increasingly challenging. Existing systems typically adopt either structure-centric methods, committing to structures determined upfront that limit fine-grained control, or orchestration-centric methods, adapting decisions dynamically while leaving coordination structure implicit and unstable. To address this challenge, we revisit multi-agent coordination from a probabilistic perspective, casting it as posterior inference over the joint distribution of structure and orchestration. We introduce MACA, an automated coordination framework that learns a task- and budget-conditioned structural prior over agent participation and interactions. This prior guides a policy-based orchestration as an approximation to posterior inference, enabling efficient solutions with fine-grained control. Across benchmarks, MACA outperforms adaptive multi-agent baselines by an average of 8.42% while using 43.19% fewer tokens. Further investigation reveals that joint adaptation of structure and orchestration suppresses redundant interactions, converging coordination toward task-effective execution.

翻译：随着基于大语言模型（LLM）的多智能体系统在处理日益复杂任务时规模不断扩展，在结构稳定性与动态适应性之间寻求平衡变得愈发具有挑战性。现有系统通常采用两种方法：一是以结构为中心的方法，采用预先确定的结构，从而限制了细粒度控制；二是以编排为中心的方法，动态调整决策，但协调结构却隐式且不稳定。为应对这一挑战，我们从概率论视角重新审视多智能体协调问题，将其视为结构与编排联合分布上的后验推理过程。我们提出MACA（Multi-Agent Coordination Adaptation，多智能体协调自适应）框架，该自动化协调框架能够学习任务与预算条件化的结构先验知识，用以约束智能体参与及交互行为。这一先验知识引导基于策略的编排作为后验推理的近似方法，从而在实现细粒度控制的同时获得高效解。在多项基准测试中，MACA在平均性能上超越自适应多智能体基线8.42%，同时减少43.19%的令牌使用量。进一步研究表明，结构与编排的联合自适应能够抑制冗余交互，使协调过程收敛至任务高效执行状态。