Trip+: Benchmarking Agents in Personalized Interactive Travel Planning
Interactive travel planning has become a popular use case for language models. Agents are deployed to manage evolving preferences and unexpected disruptions over multiple turns. Such settings require models to make complex, profile-conditioned planning decisions. However, existing benchmarks often evaluate feasibility, personalization, or interaction in relatively isolated settings. We therefore introduce Trip+ to measure the ability of agents to plan travel holistically. In Trip+, given traveler profiles and dynamic interactions, agents must generate and revise minute-level itineraries. End-to-end traveler experiences are evaluated via an LLM-based simulator, enabling the assessment of s...
| Authors | Junle Chen, Wei Chen, Yehong Xu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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