用三阶段框架让旅行规划更智能高效，约束满足率超97%
TriFlow: A Progressive Multi-Agent Framework for Intelligent Trip Planning
- 分检索、规划、治理三阶段逐步缩小搜索空间
- 在两个基准上达成91.1%~97%的可行计划通过率
- 适合需要精准行程生成的AI应用开发者
真实世界旅行规划需将开放性用户需求转化为符合空间、时间与预算约束的可执行行程，并匹配用户偏好。现有基于大模型的智能体在约束满足、工具协同与效率方面表现不佳，常生成不可行或高成本方案。为此，我们提出TriFlow，一种渐进式多智能体框架，通过检索、规划、治理三阶段管道，融合结构化推理与语言灵活性。该设计逐步缩小搜索空间，借助规则与大模型协作生成满足约束的行程，并通过有界迭代优化确保全局可行性与个性化。在TravelPlanner与TripTailor基准上的评估显示，其达到91.1%和97%的最终通过率，相比当前最先进方法运行效率提升逾10倍。
原文摘要 · Abstract (English)
Real-world trip planning requires transforming open-ended user requests into executable itineraries under strict spatial, temporal, and budgetary constraints while aligning with user preferences. Existing LLM-based agents struggle with constraint satisfaction, tool coordination, and efficiency, often producing infeasible or costly plans. To address these limitations, we present TriFlow, a progressive multi-agent framework that unifies structured reasoning and language-based flexibility through a three-stage pipeline of retrieval, planning, and governance. By this design, TriFlow progressively narrows the search space, assembles constraint-consistent itineraries via rule-LLM collaboration, and performs bounded iterative refinement to ensure global feasibility and personalisation. Evaluations on TravelPlanner and TripTailor benchmarks demonstrated state-of-the-art results, achieving 91.1% and 97% final pass rates, respectively, with over 10x runtime efficiency improvement compared to current SOTA.
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