发表机构
Microsoft; IIT Bhubaneswar（微软公司; 布巴内斯瓦尔印度理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
TRIPPULSE是基于评论推理的多智能体旅行规划框架，分解行程生成任务为多智能体并结合10万+评论与RGPA指标，可兼顾约束满足与个性化体验。
AI 中文摘要
旅行行程生成需要平衡严格的时空约束与人类偏好。现有基于大语言模型（LLM）的规划器主要依赖结构化属性与预定义的旅行者角色，但真实的旅行决策往往受评论影响，这些评论揭示了结构化数据库中缺失的体验因素，如舒适度、安全性、服务质量、氛围、拥挤度及隐藏风险。因此，整合此类评论信息对于生成贴合现实、以用户为中心的行程至关重要。我们提出TRIPPULSE，这是一个基于评论推理的多智能体旅行规划框架。该框架不依赖单一规划器（会面临上下文与推理瓶颈），而是将行程生成分解为专门的智能体，每个智能体在局部上下文下运作，分别负责住宿、交通、餐饮、景点与活动，并通过具有调度机制的全局协调器进行协调，确保时间与预算可行性。我们在TRIPCRAFT基础上扩充了10万+真实评论，并引入了基于评论的角色对齐（RGPA），这是一种作为评判者的LLM指标，用于评估与以人类为中心的旅行体验的对齐程度。针对不同旅行时长、多样的专有与开源模型开展的实验显示，TRIPPULSE在保持强约束满足的同时，生成了更具个性化、更贴合体验的行程。
英文摘要
Travel itinerary generation requires balancing strict spatio-temporal constraints with human preferences. Existing LLM-based planners mainly rely on structured attributes and pre- defined traveler personas, but real travel deci- sions are often shaped by reviews that reveal experiential factors such as comfort, safety, ser- vice quality, ambiance, crowding, and hidden risks absent from structured databases. Incor- porating such review information is therefore critical to realistic, user-centric itinerary gen- eration. We propose TRIPPULSE1, a multi- agent framework for review-grounded travel planning. Instead of relying on a monolithic planner (and face context and reasoning bot- tlenecks), TRIPPULSE2 decomposes itinerary generation into specialized agents (each op- erating over localized contexts) for accom- modations, transportation, meals, attractions, and events, coordinated through a global or- chestrator with scheduling mechanisms that enforce temporal and budget feasibility. We augment TRIPCRAFT with 100K+ real-world reviews and introduce Review-Grounded Per- sona Alignment (RGPA), an LLM-as-a-Judge metric for evaluating alignment with human- centric travel experiences. Experiments across multiple trip durations and diverse proprietary and open-source models show that TRIPPULSE maintains strong constraint satisfaction while generating more personalized and experien- tially grounded itineraries.
Comments31 pages, EMNLP 2026
