@inproceedings{chen-etal-2026-trip,
title = "Trip+: Benchmarking Agents in Personalized Interactive Travel Planning",
author = "Chen, Junle and
Chen, Wei and
Xu, Yehong and
Huang, Zhengjun and
Wu, Yuqian and
Tian, Zhoujin and
Wang, Kai and
Chen, Rui and
Zhou, Xiaofang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1384/",
pages = "31588--31625",
ISBN = "979-8-89176-459-0",
abstract = "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 LLM-based simulators, enabling the assessment of subjective metrics like fatigue. Our scenarios range from simple request resolutions to complex environment-driven replanning. We evaluate 18 LMs and find a consistent gap in experiential quality. Models favor technically feasible but exhausting itineraries that show weaker profile alignment."
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<abstract>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 LLM-based simulators, enabling the assessment of subjective metrics like fatigue. Our scenarios range from simple request resolutions to complex environment-driven replanning. We evaluate 18 LMs and find a consistent gap in experiential quality. Models favor technically feasible but exhausting itineraries that show weaker profile alignment.</abstract>
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%0 Conference Proceedings
%T Trip+: Benchmarking Agents in Personalized Interactive Travel Planning
%A Chen, Junle
%A Chen, Wei
%A Xu, Yehong
%A Huang, Zhengjun
%A Wu, Yuqian
%A Tian, Zhoujin
%A Wang, Kai
%A Chen, Rui
%A Zhou, Xiaofang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F chen-etal-2026-trip
%X 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 LLM-based simulators, enabling the assessment of subjective metrics like fatigue. Our scenarios range from simple request resolutions to complex environment-driven replanning. We evaluate 18 LMs and find a consistent gap in experiential quality. Models favor technically feasible but exhausting itineraries that show weaker profile alignment.
%U https://aclanthology.org/2026.emnlp-main.1384/
%P 31588-31625
Markdown (Informal)
[Trip+: Benchmarking Agents in Personalized Interactive Travel Planning](https://aclanthology.org/2026.emnlp-main.1384/) (Chen et al., EMNLP 2026)
ACL
- Junle Chen, Wei Chen, Yehong Xu, Zhengjun Huang, Yuqian Wu, Zhoujin Tian, Kai Wang, Rui Chen, and Xiaofang Zhou. 2026. Trip+: Benchmarking Agents in Personalized Interactive Travel Planning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 31588–31625, Budapest, Hungary. Association for Computational Linguistics.