用于智能工作流合成的拓扑与执行耦合分层搜索

中文摘要 AI 辅助

尽管结构化工作流使大语言模型能够解决复杂问题，但其创建自动化因庞大的组合搜索空间而严重受阻，常导致不灵活且资源密集的离线训练依赖。为解决此问题，我们将工作流生成概念化为拓扑与执行交织的搜索范式，其中更广泛的拓扑层决定子任务边界，较低级别的执行结果会积极重塑拓扑本身。在此基础上，我们引入了HierFlow，一种无需训练、测试时的分层搜索架构，通过将反馈引导的拓扑调整与受MCTS启发的快速树搜索相结合，实现智能工作流设计的自动化，以优化子工作流。HierFlow通过智能门控模块提高效率，并通过深入分析不同程度的跨任务耦合对分层拆分有效性的影响来进一步支持该机制。在问答、数学推理和代码生成基准测试中的全面测试证实，HierFlow始终优于强大的基线，在无额外训练开销的情况下实现了高质量结果与计算效率的最佳平衡。

英文摘要

Although structured workflows empower Large Language Models (LLMs) to tackle complex problems, automating their creation is severely hindered by a vast combinatorial search space, frequently resulting in inflexible and resource-heavy offline training dependencies. To address this, we conceptualize workflow generation as an intertwined topology-and-execution search paradigm, where the broader topological layer dictates subtask boundaries and lower-level execution outcomes actively reshape the topology itself. Building on this foundation, we introduce HierFlow, a training-free, test-time hierarchical search architecture that automates agentic workflow design by merging feedback-guided topology adjustments with a fast, MCTS-inspired tree search for sub-workflow optimization. HierFlow maximizes efficiency through an intelligent gating module that selectively triggers execution-level searches based on contextual necessity, a mechanism we further support with an in-depth analysis detailing how varying degrees of cross-task coupling impact the effectiveness of hierarchical splitting. Comprehensive testing across question answering, mathematical reasoning, and code generation benchmarks confirms that HierFlow consistently outperforms strong baselines, delivering an optimal balance of high-quality results and computational efficiency without any additional training overhead.

发表机构

• Baylor University（贝勒大学）
• NEC Laboratories America（美国NEC实验室）
• University of Arkansas（阿肯色大学）
• Southern Illinois University（南伊利诺伊大学）

机构由 AI 辅助整理，请以论文原文为准。