Problem
Research questions and friction points this paper is trying to address.
travel planning
reviews
spatio-temporal constraints
personal preferences
experiential factors
TRIPPULSE: Multi-Agent Travel Planning with Review-Grounded Reasoning
🤖 AI Summary
为解决旅行规划中结合体验因素的问题，TRIPPULSE通过多代理框架和基于评论的角色对齐方法生成更个性化、符合用户体验的行程。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
multi-agent framework
review-grounded reasoning
persona alignment
context decomposition
LLM-as-a-Judge
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