MAGIC：混合粒度智能体图构建框架
MAGIC: Mixed-Granularity Agent Graphs via Incremental Construction with Dense-Reward Reinforcement Learning
多智能体协作拓扑不再是全细粒度或全分组，MAGIC 逐角色混合选粒度，八个基准上跑赢了现有基线，做 Agent 系统的可以看看。
MAGIC 是一个用强化学习生成 LLM 多智能体协作图的框架，核心是为每个功能角色局部选择粒度，可实例化为单个智能体或可复用的智能体组。该框架通过增量式构建混合粒度的智能体图，并使用基于势函数的奖励塑形从探测效用和结构信号中提供中间反馈。MAGIC 在八个基准上超越了现有最先进的拓扑生成基线，同时保持较强的推理效率。
MAGIC: Mixed-Granularity Agent Graphs via Incremental Construction with Dense-Reward Reinforcement Learning
Collaboration topology shapes both the performance and execution cost of LLM-based multi-agent systems. Because tasks differ in complexity and required capabilities, recent approaches generate task-specific collaboration graphs that specify agent participation and information flow. However, representative topology generators use either individual agents or predefined groups throughout an organization, overlooking differing collaboration needs across subtasks. Our key insight is to select granularity locally for each functional role, combining fine-grained control with reusable collaboration patterns within one organization. Learning such organizations requires exploring a combinatorial construction space with limited intermediate feedback from final-answer rewards. Therefore, we propose MAGIC, a dense-reward reinforcement learning framework for mixed-granularity graph generation. Specifically, MAGIC constructs a mixed-granularity agent graph by sequentially selecting a functional role, instantiating it as a single agent or reusable group, and connecting it to existing units. We directly optimize the construction policy using returns from trajectories sampled under the current policy and use potential-based reward shaping to provide intermediate feedback from probe-based utility and structural signals while preserving the cumulative task reward. MAGIC outperforms state-of-the-art baselines across eight benchmarks and demonstrates strong inference efficiency in our efficiency study.
