ProgRouter: Online Progress-Guided Orchestration for Multi-Agent LLM Workflows under Quality-Cost Tradeoffs· ProgRouter: 多智能体大模型在线路由框架
Multi-agent large language model (LLM) workflows have emerged as a powerful paradigm for solving complex, open-ended tasks through collaborative reasoning among specialized LLM agents, but they incur substantial operating costs due to repeated LLM invocations and long-horizon context accumulation. Existing cascade routing methods make one-shot, query-level decisions and cannot adapt to the dynamic, state-dependent nature of multi-step workflows, in which the right LLM at each step depends on evolving task progress, remaining task difficulty, and cost-efficiency requirements. We present ProgRouter, an online progress-guided routing framework that adaptively selects LLM agents across workflow steps to preserve task-solving quality while adhering to time and cost budgets. ProgRouter introduce
多智能体大模型在线路由框架，实现成本与质量之间的平衡。
- 核心方法
- 提出ProgRouter框架，该框架能够根据任务进展动态选择合适的大模型智能体，从而在保持任务解决质量的同时，满足时间和成本预算。
- 适合谁读
- 研究者 / 工程师
- 要解决的问题
- 现有的多智能体大模型工作流中，由于重复调用大模型和长时间累积上下文，导致操作成本高昂；而现有的级联路由方法无法适应多步骤任务解决过程中任务状态和成本效益要求的变化。
- 关键实验
- 在多个复杂任务上进行了实验，验证了ProgRouter相比传统方法在成本控制和任务质量保持上的优势。
- 主要贡献
- 1. 设计了一个能够实时适应任务进展的在线路由框架；2. 通过实验验证了在不同成本预算下，ProgRouter能够有效平衡任务解决质量和成本。
- 意义与局限
- 为多智能体大模型的应用提供了成本控制的解决方案，具有重要的经济意义，但可能需进一步优化性能。