让多个AI-agent在有限算力下高效协作，提升推理表现。
FutureWeaver: Planning Test-Time Compute for Multi-Agent Systems with Modularized Collaboration
- 用可复用的协作模块自动提炼多智能体交互模式。
- 双层规划架构在预算内优化短程动作与长程策略。
- 适用于需要高效算力分配的复杂多智能体任务。
扩展测试时计算已被证明能显著提升大语言模型性能而无需额外训练。然而，将此类技术应用于多智能体系统仍面临挑战：现有方法缺乏合理机制来分配计算以实现有效协作，难以扩展协调能力，也无法在明确预算约束下优化算力使用。为此，我们提出 FutureWeaver，一个在固定预算下规划和优化多智能体系统测试时计算分配的框架。该框架引入协作模块，形式化为可调用的模块化函数，封装可复用的多智能体工作流，并通过自对弈反思从重复交互模式中自动提取。基于这些模块，采用双层规划架构，联合执行短期动作选择与长期抽象前瞻，以在预算约束下优化推理轨迹。在复杂智能体基准上的实验表明，FutureWeaver 在多种预算设置下均持续优于基线，验证了其在推理时优化多智能体协作的有效性。
原文摘要 · Abstract (English)
Scaling test-time computation has been shown to significantly improve large language model (LLM) performance without additional training. However, extending these techniques to multi-agent systems remains challenging: existing approaches lack principled mechanisms for allocating compute to enable effective collaboration, scaling coordination itself, or optimizing compute usage under explicit budget constraints. To address this gap, we propose FutureWeaver, a framework for planning and optimizing test-time compute allocation in multi-agent systems under fixed budgets. It introduces collaboration modules, formalized as modular, callable functions that encapsulate reusable multi-agent workflows and are automatically induced via self-play reflection from recurring interaction patterns. Building on these modules, it employs \emph{a dual-level planning architecture} that jointly performs short-horizon action selection and long-horizon abstract lookahead to optimize inference trajectories under budget constraints. Experiments on complex agent benchmarks demonstrate that FutureWeaver consistently outperforms baselines across diverse budget settings, validating its effectiveness for multi-agent collaboration in inference-time optimization.
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