TRIPPULSE: Multi-Agent Travel Planning with Review-Grounded Reasoning
2026-08-31 • Computation and Language
Computation and Language
AI summaryⓘ
The authors designed a new system called TRIPPULSE to help create travel plans that better match real human experiences by using online reviews, not just structured data like location and time. Their system breaks down the planning into smaller expert agents for hotels, transport, food, attractions, and events, which work together to keep schedules and budgets reasonable. They also developed a way to measure how well these plans reflect real traveler preferences using a large set of reviews. Tests showed that TRIPPULSE makes plans that fit constraints well while feeling more personalized and realistic.
Travel itinerary generationLarge Language Models (LLMs)Spatio-temporal constraintsUser preferencesMulti-agent systemsReview analysisPersona alignmentScheduling mechanismsConstraint satisfactionExperiential factors
Authors
Priyanshu Karmakar, Borru Vijay Sai, Shubhojit Mallick, Abhik Jana, Shreya Ghosh, Manish Gupta
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.