Travel Planning

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21 papers0 papers in the last four weeks, down 100% on the four weeks before. 0.0% of all new papers.

Replan, Repair, or Edit? A Unified Empirical Evaluation of Travel Agents for Itinerary Revision under Resource Disruptions
Sep 17, 2026Xiaofei Yuan, Yan Zhang, Shaobo Qiao +7LLM Planning Travel Planning
Euler AI · University of New South Wales · University of Sydney +1
CALM: A Calibrated LLM Choice Network Framework for Activity-Based Traveler Simulation
Sep 6, 2026Yezhou ChengHuman Behavior Simulation Travel Planning
Independent Researcher
TRIPPULSE: Multi-Agent Travel Planning with Review-Grounded Reasoning
Aug 31, 2026Priyanshu Karmakar, Borru Vijay Sai, Shubhojit Mallick +3Multi-Agent LLM Systems Travel Planning
IIT Bhubaneswar, India · Microsoft, India
Behavior2Trip: Towards Personalized Travel Planning via User Behavior Trajectory
Aug 27, 2026Zihao Cheng, Yingyu Shan, Hongru Wang +6Travel Planning User Preference Modeling
School of Computer Science and Engineering, Beihang University, Beijing · Beijing Institute of Technology · University of Edinburgh +1
TREK: A Travel Reasoning and Evaluation Kit for LLM Agents in Complex Trip Planning
Jul 29, 2026Jinhu Qi, Wentao Zhang, Siu Man Ng +4Tool-Use Evaluation LLM Agent Evaluation
The Chinese University of Hong Kong Hong Kong, China · Macao Polytechnic University Macau, China
AI Tour Meeting: Group Travel Planning by LLM Agents
Jul 21, 2026Daisuke KikutaMulti-Agent LLM Systems LLM Planning
NTT, Inc.
From Feasibility to Desirability: Plan, Learn, Adapt (PLA) Framework for Personalized On-Device Itinerary Generation
Jul 17, 2026Himel Dev, Tanmoy Sen, Madhusudan Basak +1Travel Planning User Preference Modeling
Tech LLC · University of Virginia · University of Massachusetts Amherst
LegalFarePlan: A Label-Setting Framework for Fare-Transparent Urban Rail Route Planning under Non-Additive Fare Rules
Jul 5, 2026Tanghui LiIntelligent Transportation Systems Travel Planning
Tongji University · Shanghai, China
Trip+: Benchmarking Agents in Personalized Interactive Travel Planning
Jun 19, 2026Junle Chen, Wei Chen, Yehong Xu +6LLM Agent Evaluation AI Agent Benchmarks
HKUST · Tencent Hy · HKUST(GZ)
TravelEval: A Comprehensive Benchmarking Framework for Evaluating LLM-Powered Travel Planning Agents
May 31, 2026Weiyi Chen, Shuaixiong Wang, Ziyun Gao +5Spatiotemporal Reasoning LLM Agent Evaluation
Zhejiang University · Hangzhou, China · Hong Kong Polytechnic University +5
GroupTravelBench: Benchmarking LLM Agents on Multi-Person Travel Planning
May 24, 2026Xiang Cheng, Yulan Hu, Lulu Zheng +4AI-Assisted Decision Making LLM Planning
Gaoling School of Artificial Intelligence, Renmin University of China · AMAP, Alibaba Group
Revisiting the Travel Planning Capabilities of Large Language Models
May 5, 2026Bo-Wen Zhang, Jin Ye, Peng-Yu Hua +4LLM Self-Correction LLM Evaluation
State Key Laboratory of Novel Software Technology, Nanjing University, China · School of Intelligence Science and Technology, Nanjing University, China · School of Artificial Intelligence, Nanjing University, China
Decoupled Travel Planning with Behavior Forest
Apr 23, 2026Duanyang Yuan, Sihang Zhou, Yanning Hou +7LLM Planning Travel Planning
College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China · College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China · College of Systems Engineering, National University of Defense Technology, Changsha 410073, China +1
Preference Guided Iterated Pareto Referent Optimisation for Accessible Route Planning
Apr 1, 2026Paolo Speziali, Arno De Greef, Mehrdad Asadi +3Travel Planning Pareto Optimization
Vrije Universiteit Brussel Brussels, Belgium · Gemeente Amsterdam Amsterdam, Netherlands
Demystifying Reinforcement Learning for Long-Horizon Tool-Using Agents: A Comprehensive Recipe
Mar 23, 2026Xixi Wu, Qianguo Sun, Ruiyang Zhang +4Travel Planning RL for Tool Use
The Chinese University of Hong Kong · IDEA Research · University of Macau
MIND: Multi-agent inference for negotiation dialogue in travel planning
Mar 23, 2026Hunmin Do, Taejun Yoon, Kiyong JungTravel Planning Multi-Agent Negotiation
School of Mechanical Engineering, Sungkyunkwan University, Suwon, Korea · Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul, Korea · Department of Software, Sungkyunkwan University, Suwon, Korea
HiMAP-Travel: Hierarchical Multi-Agent Planning for Long-Horizon Constrained Travel
Mar 5, 2026The Viet Bui, Wenjun Li, Yong LiuTravel Planning Long-Horizon LLM Agents
Singapore Management University
CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agents
Nov 4, 2025Jiayu Liu, Cheng Qian, Zhaochen Su +4LLM Planning LLM Agent Evaluation
The Hong Kong University of Science and Technology · University of Illinois Urbana-Champaign · Tsinghua University
TripScore: Aligning LLMs for Real-World Travel Planning via Expert-Calibrated Reward
Oct 10, 2025Yincen Qu, Huan Xiao, Feng Li +5Reward Modeling LLM Planning
Trip.com Group · Chongqing University · Stanford University
DeepTravel: An End-to-End Agentic Reinforcement Learning Framework for Autonomous Travel Planning Agents
Sep 26, 2025Yansong Ning, Rui Liu, Jun Wang +6Agentic RL Travel Planning
The Hong Kong University of Science and Technology (Guangzhou) · Didichuxing Co. Ltd
Decomposability-Guided Cooperative Coevolution for Large-Scale Itinerary Planning
Jun 6, 2025Ziyu Zhang, Peilan Xu, Yuetong Sun +2Travel Planning Traveling Salesperson Problem
School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China. · Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China. · School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, Guangdong, China. +1

Sep 17, 2026J/K move · Enter open · S save

Replan, Repair, or Edit? A Unified Empirical Evaluation of Travel Agents for Itinerary Revision under Resource Disruptions

Xiaofei Yuan, Yan Zhang, Shaobo Qiao, Huangleshuai He+6

Euler AI · University of New South Wales · University of Sydney+1

LLM Planning Travel Planning LLM Agent Planning

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Travel-planning agents generate itineraries that may become infeasible after acceptance because of flight cancellations, hotel unavailability, or attraction closures. Revising these itineraries involves full replanning, classical plan repair, and LLM-based travel-agent revision, whose differing task formulations and evaluation protocols hinder comparison. We conduct a systematic empirical study using two TREK-derived benchmark sets: 500 single-disruption cases, including feasible and infeasible instances, and 200 feasible simultaneous compound-disruption cases. We compare LLM-Z3 full replanning, IPyHOPPER hierarchical repair, and an iTIMO local-revision adapter across effectiveness, plan stability, and computational cost. LLM-Z3 with Gemini achieved the highest observed compound-disruption success. IPyHOPPER nearly matched that configuration's single-disruption overall success, while preserving substantially more of the accepted itinerary on successful repairs. Successful hierarchical and local repairs made fewer edits and retained more accepted commitments than full replanning. Computational profiles differed: IPyHOPPER used no LLM inference, the evaluated LLM-Z3 adapter used compact one-call inference, and the iTIMO adapter consumed substantially more tokens. The study provides practical guidelines for balancing feasibility recovery, commitment preservation, and computational cost within evaluated settings.