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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 8 Pith papers
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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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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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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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Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications
The paper defines urban LLM agents, surveys their sensing, memory, reasoning, execution, and learning workflows, and organizes their applications across planning, transportation, environment, safety, and society.
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AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need
A divide-and-conquer multi-agent framework with task forests and specialized roles improves math and code benchmarks but not commonsense or domain QA, and the adaptive heterogeneous-LLM engine is never tested.
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