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TravelAgent: An AI Assistant for Personalized Travel Planning
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As global tourism expands and artificial intelligence technology advances, intelligent travel planning services have emerged as a significant research focus. Within dynamic real-world travel scenarios with multi-dimensional constraints, services that support users in automatically creating practical and customized travel itineraries must address three key objectives: Rationality, Comprehensiveness, and Personalization. However, existing systems with rule-based combinations or LLM-based planning methods struggle to fully satisfy these criteria. To overcome the challenges, we introduce TravelAgent, a travel planning system powered by large language models (LLMs) designed to provide reasonable, comprehensive, and personalized travel itineraries grounded in dynamic scenarios. TravelAgent comprises four modules: Tool-usage, Recommendation, Planning, and Memory Module. We evaluate TravelAgent's performance with human and simulated users, demonstrating its overall effectiveness in three criteria and confirming the accuracy of personalized recommendations.
Forward citations
Cited by 15 Pith papers
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SPAGBias reveals that LLMs form nuanced gender associations with specific urban micro-spaces that exceed real-world distributions and produce failures in planning and descriptive tasks.
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Social Dynamics as Critical Vulnerabilities that Undermine Objective Decision-Making in LLM Collectives
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AlterAtlas: Shifting Travel Planning from AI Generation to Validation via Persona-Driven Simulations
AlterAtlas replaces one-shot AI itinerary generation with an interactive validation loop where persona-driven simulations expose route-level constraints and guide iterative revision.
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TravelEval: A Comprehensive Benchmarking Framework for Evaluating LLM-Powered Travel Planning Agents
TravelEval is a new benchmark with a six-dimensional evaluation framework, realistic data sandbox, and simulation-based global assessment for LLM-powered travel planning agents.
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MobilityBench: A Benchmark for Evaluating Route-Planning Agents in Real-World Mobility Scenarios
MobilityBench is a 100,000-episode benchmark with a replay sandbox for deterministic evaluation of LLM route-planning agents; current models score well on basic tasks but fail preference-constrained routing.
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iTIMO: An LLM-empowered Synthesis Dataset for Travel Itinerary Modification
iTIMO is the first benchmark for travel itinerary modification, built by LLM-driven perturbation of real-world itineraries across three operations and three disruption intents.
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OpenReward: Learning to Reward Long-form Agentic Tasks via Reinforcement Learning
A tool-augmented reward model trained with GRPO on 27K synthetic pairs beats existing reward models on long-form QA judgment and improves downstream alignment.
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RETAIL: Towards Real-world Travel Planning for Large Language Models
A new travel-planning benchmark and multi-agent system that still mostly fails, with the best system passing only 2.72% of test cases.
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ChinaTravel: An Open-Ended Travel Planning Benchmark with Compositional Constraint Validation for Language Agents
ChinaTravel is a benchmark with sandbox, compositional DSL, and 1154-human dataset for testing language agents on open-ended travel planning constraint satisfaction.
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Agentic AI for Trip Planning Optimization Application
An orchestrated multi-agent AI framework for trip planning optimization paired with a new ground-truth dataset achieves 77.4% accuracy on the TOP Benchmark, outperforming single-agent and workflow baselines.
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Emergent Social Intelligence Risks in Generative Multi-Agent Systems
Generative multi-agent systems exhibit emergent collusion and conformity behaviors that cannot be prevented by existing agent-level safeguards.
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TripTailor: A Real-World Benchmark for Personalized Travel Planning
A travel-planning benchmark is claimed in the abstract, but the full text is an unrelated supernova spectroscopy paper, leaving the central claim completely unsupported.
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