TRIPPULSE: Multi-Agent Travel Planning with Review-Grounded Reasoning
cs.CL
Submitted: 2026-08-31
Updated: 2026-08-31
Comments: 31 pages, EMNLP 2026
Code: https://github.com/VijaySaiBorru/TripPulse
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
The gist: Travel itinerary generation requires balancing strict spatio-temporal constraints with human preferences.
Terminology
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.
Sources
- The Llama 3 Herd of Models
- LLM+P: Empowering Large Language Models with Optimal Planning Proficiency
- Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs
- Qwen2.5 Technical Report
- OpenAI GPT-5 System Card
- Voyager: An Open-Ended Embodied Agent with Large Language Models
- Learning to Shop Like Humans: A Review-driven Retrieval-Augmented Recommendation Framework with LLMs
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