Large language models are increasingly deployed as multi-agent systems, where specialized roles communicate and collaborate through structured interactions to solve complex tasks that often exceed the capacity of a single agent. However, most existing systems still rely on a fixed role library and an execution-frozen interaction topology, a rigid design choice that frequently leads to task mismatch, prevents timely adaptation when new evidence emerges during reasoning, and further inflates inference cost. We introduce MetaGen, a training-free framework that adapts both the role space and the collaboration topology at inference time, without updating base model weights. MetaGen generates and rewrites query-conditioned role specifications to maintain a controllable dynamic role pool, then instantiates a constrained execution graph around a minimal backbone. During execution, it iteratively updates role prompts and adjusts structural decisions using lightweight feedback signals. Experiments on code generation and multi-step reasoning benchmarks show that MetaGen improves the accuracy and cost tradeoff over strong multi-agent baselines.

翻译：大语言模型正日益以多智能体系统的形式部署，其中专门化的角色通过结构化交互进行通信与协作，以解决通常超出单个智能体能力的复杂任务。然而，现有系统大多仍依赖于固定的角色库和冻结执行的交互拓扑结构。这种僵化的设计选择常导致任务不匹配，在推理过程中出现新证据时无法及时适应，并进一步推高推理成本。本文提出MetaGen，一种无需训练的框架，可在推理时自适应调整角色空间与协作拓扑结构，且无需更新基础模型权重。MetaGen通过生成并重写查询条件化的角色描述来维护可控的动态角色池，随后围绕最小骨干网络实例化一个受约束的执行图。在执行过程中，它利用轻量级反馈信号迭代更新角色提示并调整结构决策。在代码生成与多步推理基准测试上的实验表明，MetaGen在准确性与成本权衡方面优于现有的强大多智能体基线方法。