让大模型一次生成跨领域智能工作流，比传统迭代方法更快更稳。
Learning to Compose for Cross-domain Agentic Workflow Generation
- 将任务分解为可复用的能力基元，再稀疏组合成定制工作流。
- 单次生成即超越需20次迭代的现有方法，成功率更高且耗时更低。
- 适合需要快速部署跨领域自动化系统的研究与工程人员。
自动生成智能工作流——可执行的操作图或代码，用于协调推理、验证与修复——已成为解决单一语言模型无法可靠处理复杂任务的有效方式。然而，优质工作流的定义高度依赖于任务分布和可用操作符。在领域迁移下，现有系统通常依赖迭代工作流优化从庞大空间中探索可行方案，导致高迭代成本并产生不稳定、领域特定的行为。为此，我们将在开源大模型中内化一种‘分解-重构-决策’机制，以实现跨领域工作流生成。通过学习跨多个领域的紧凑可复用工作流能力集实现分解；将每个输入任务映射到这些基元的稀疏组合，实现单次生成任务特定工作流；通过反事实贡献分析判断各能力对生成成败的边际影响，从而识别真正驱动成功的模块。在严格的多领域、跨领域及未见领域评估中，我们的单次生成器显著优于需20次迭代的当前最优迭代基线，同时大幅降低生成延迟与成本。
原文摘要 · Abstract (English)
Automatically generating agentic workflows -- executable operator graphs or codes that orchestrate reasoning, verification, and repair -- has become a practical way to solve complex tasks beyond what single-pass LLM generation can reliably handle. Yet what constitutes a good workflow depends heavily on the task distribution and the available operators. Under domain shift, current systems typically rely on iterative workflow refinement to discover a feasible workflow from a large workflow space, incurring high iteration costs and yielding unstable, domain-specific behavior. In response, we internalize a decompose-recompose-decide mechanism into an open-source LLM for cross-domain workflow generation. To decompose, we learn a compact set of reusable workflow capabilities across diverse domains. To recompose, we map each input task to a sparse composition over these bases to generate a task-specific workflow in a single pass. To decide, we attribute the success or failure of workflow generation to counterfactual contributions from learned capabilities, thereby capturing which capabilities actually drive success by their marginal effects. Across stringent multi-domain, cross-domain, and unseen-domain evaluations, our 1-pass generator surpasses SOTA refinement baselines that consume 20 iterations, while substantially reducing generation latency and cost.
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