无需标注数据，自动优化任务流程的智能代理
Polymath: A Self-Optimizing Agent with Dynamic Hierarchical Workflow
- 用代码表示任务流图，支持动态层级调整
- 在6个基准上平均提升8.1%，超越现有方法
- 适合需要自适应处理复杂任务的场景
大型语言模型通过执行由详细指令和结构化操作组成的代理工作流，在解决复杂任务方面表现出色。然而，通过文本接口手动将基础模型嵌入链式思维、自我反思和ReACT等代理系统，限制了通用代理的可扩展性和效率。近期研究尝试通过基于代码的表示自动化生成和优化工作流，但现有方法通常依赖标注数据进行训练和优化，难以应对真实世界中无标注数据的动态问题。为此，我们提出Polymath，一种具有动态分层工作流的自优化代理，利用任务流图的灵活性和代码表示工作流的表达能力，解决广泛的现实动态问题。其优化方法融合多网格启发式图优化与自反思引导的进化算法，在无标注数据条件下持续优化工作流。在涵盖编程、数学和多轮问答任务的六个基准数据集上的实验结果表明，Polymath相较当前最优基线平均提升8.1%。
原文摘要 · Abstract (English)
Large language models (LLMs) excel at solving complex tasks by executing agentic workflows composed of detailed instructions and structured operations. Yet, building general-purpose agents by manually embedding foundation models into agentic systems such as Chain-of-Thought, Self-Reflection, and ReACT through text interfaces limits scalability and efficiency. Recently, many researchers have sought to automate the generation and optimization of these workflows through code-based representations. However, existing methods often rely on labeled datasets to train and optimize workflows, making them ineffective and inflexible for solving real-world, dynamic problems where labeled data is unavailable. To address this challenge, we introduce Polymath, a self-optimizing agent with dynamic hierarchical workflow that leverages the flexibility of task flow graphs and the expressiveness of code-represented workflows to solve a wide range of real-world, dynamic problems. The proposed optimization methodology integrates multi-grid-inspired graph optimization with a self-reflection-guided evolutionary algorithm to refine workflows without labeled data. Experimental results on six benchmark datasets across coding, math, and multi-turn QA tasks show that Polymath achieves 8.1% average improvement over state-of-the-art baselines.
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