iTIMO: An LLM-empowered Synthesis Dataset for Travel Itinerary Modification

• URL: http://arxiv.org/abs/2601.10609v2
• Date: Thu, 22 Jan 2026 05:23:03 GMT
• Title: iTIMO: An LLM-empowered Synthesis Dataset for Travel Itinerary Modification
• Authors: Zhuoxuan Huang, Yunshan Ma, Hongyu Zhang, Hua Ma, Zhu Sun,
• Abstract summary: iTIMO is a pipeline that frames the generation of need-to-modify itinerary data as an intent-driven perturbation task.<n>It instructs large language models to perturb real-world itineraries using three operations: REPLACE, ADD, and DELETE.<n>Overall, iTIMO provides a comprehensive testbed for the modification task, and empowers the evolution of traditional travel recommender systems.
• Score: 20.2135943012742
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Addressing itinerary modification is crucial for enhancing the travel experience as it is a frequent requirement during traveling. However, existing research mainly focuses on fixed itinerary planning, leaving modification underexplored due to the scarcity of need-to-modify itinerary data. To bridge this gap, we formally define the itinerary modification task and propose a general pipeline to construct the corresponding dataset, namely iTIMO. This pipeline frames the generation of need-to-modify itinerary data as an intent-driven perturbation task. It instructs large language models to perturb real-world itineraries using three operations: REPLACE, ADD, and DELETE. Each perturbation is grounded in three intents: disruptions of popularity, spatial distance, and category diversity. Furthermore, hybrid evaluation metrics are introduced to ensure perturbation effectiveness. We conduct comprehensive benchmarking on iTIMO to analyze the capabilities and limitations of state-of-the-art LLMs. Overall, iTIMO provides a comprehensive testbed for the modification task, and empowers the evolution of traditional travel recommender systems into adaptive frameworks capable of handling dynamic travel needs. Dataset, code and supplementary materials are available at https://github.com/zelo2/iTIMO.

Related papers

• TravelBench: A Broader Real-World Benchmark for Multi-Turn and Tool-Using Travel Planning [22.3041021610283]
  Travel planning is a natural real-world task to test large language models (LLMs) planning and tool-use abilities.<n>TravelBench is a benchmark for fully real-world travel planning.
  arXiv  Detail & Related papers  (2025-12-27T18:25:14Z)
• TripTide: A Benchmark for Adaptive Travel Planning under Disruptions [8.592189274445149]
  TripTide is the first benchmark evaluating Large Language Models' ability to revise under realistic disruptions.<n>Our experiments show that LLMs maintain strong sequential consistency and semantic stability, while spatial deviations are larger for shorter trips but decrease with longer ones.<n>TripTide establishes a benchmark for evaluating adaptability, personalization, and resilience in LLM-based travel planning under real-world uncertainty.
  arXiv  Detail & Related papers  (2025-10-24T10:39:55Z)
• ATLAS: Constraints-Aware Multi-Agent Collaboration for Real-World Travel Planning [53.065247112514534]
  ATLAS is a general multi-agent framework designed to handle complex nature of constraints awareness in real-world travel planning tasks.<n>We demonstrate state-of-the-art performance on the TravelPlanner benchmark, improving the final pass rate from 23.3% to 44.4% over its best alternative.
  arXiv  Detail & Related papers  (2025-09-29T23:23:52Z)
• Continual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting [70.83781268763215]
  Vision-language models (VLMs) have achieved impressive performance across diverse multimodal tasks by leveraging large-scale pre-training.<n>VLMs face unique challenges such as cross-modal feature drift, parameter interference due to shared architectures, and zero-shot capability erosion.<n>This survey aims to serve as a comprehensive and diagnostic reference for researchers developing lifelong vision-language systems.
  arXiv  Detail & Related papers  (2025-08-06T09:03:10Z)
• TripTailor: A Real-World Benchmark for Personalized Travel Planning [28.965273870656446]
  TripTailor is a benchmark for personalized travel planning in real-world scenarios.<n>This dataset features over 500,000 real-world points of interest (POIs) and nearly 4,000 diverse travel itineraries.<n>We identify several critical challenges in travel planning, including the feasibility, rationality, and personalized customization.
  arXiv  Detail & Related papers  (2025-08-02T16:44:02Z)
• TP-RAG: Benchmarking Retrieval-Augmented Large Language Model Agents for Spatiotemporal-Aware Travel Planning [39.934634038758404]
  This paper introduces TP-RAG, the first benchmark tailored retrieval-augmentedtemporalRAG-aware travel planning.<n>Our dataset includes 2,348 real-world travel queries, 85,575 fine-grain POIs, 18,784 annotated POIs.
  arXiv  Detail & Related papers  (2025-04-11T17:02:40Z)
• TripCraft: A Benchmark for Spatio-Temporally Fine Grained Travel Planning [7.841787597078323]
  TripCraft establishes a new benchmark for LLM driven personalized travel planning, offering a more realistic, constraint aware framework for itinerary generation.<n>Our parameter informed setting significantly enhances meal scheduling, improving the Temporal Meal Score from 61% to 80% in a 7 day scenario.
  arXiv  Detail & Related papers  (2025-02-27T20:33:28Z)
• ME-CPT: Multi-Task Enhanced Cross-Temporal Point Transformer for Urban 3D Change Detection [10.15947374827254]
  By utilizing multi-temporal ALS point clouds, semantic changes in urban area can be captured.<n>Existing 3D change detection methods struggle to efficiently extract multi-class semantic information and change features.<n>We propose the Multi-task Enhanced Cross-temporal Point Transformer (ME-CPT) network.
  arXiv  Detail & Related papers  (2025-01-23T13:07:41Z)
• DeTra: A Unified Model for Object Detection and Trajectory Forecasting [68.85128937305697]
  Our approach formulates the union of the two tasks as a trajectory refinement problem. To tackle this unified task, we design a refinement transformer that infers the presence, pose, and multi-modal future behaviors of objects. In our experiments, we observe that ourmodel outperforms the state-of-the-art on Argoverse 2 Sensor and Open dataset.
  arXiv  Detail & Related papers  (2024-06-06T18:12:04Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• Generalized Face Forgery Detection via Adaptive Learning for Pre-trained Vision Transformer [54.32283739486781]
  We present a textbfForgery-aware textbfAdaptive textbfVision textbfTransformer (FA-ViT) under the adaptive learning paradigm. FA-ViT achieves 93.83% and 78.32% AUC scores on Celeb-DF and DFDC datasets in the cross-dataset evaluation.
  arXiv  Detail & Related papers  (2023-09-20T06:51:11Z)
• Temporal Action Localization with Enhanced Instant Discriminability [66.76095239972094]
  Temporal action detection (TAD) aims to detect all action boundaries and their corresponding categories in an untrimmed video. We propose a one-stage framework named TriDet to resolve imprecise predictions of action boundaries by existing methods. Experimental results demonstrate the robustness of TriDet and its state-of-the-art performance on multiple TAD datasets.
  arXiv  Detail & Related papers  (2023-09-11T16:17:50Z)
• Implicit and Efficient Point Cloud Completion for 3D Single Object Tracking [9.372859423951349]
  We introduce two novel modules, i.e., Adaptive Refine Prediction (ARP) and Target Knowledge Transfer (TKT) Our model achieves state-of-the-art performance while maintaining a lower computational consumption.
  arXiv  Detail & Related papers  (2022-09-01T15:11:06Z)
• Lifelong Unsupervised Domain Adaptive Person Re-identification with Coordinated Anti-forgetting and Adaptation [127.6168183074427]
  We propose a new task, Lifelong Unsupervised Domain Adaptive (LUDA) person ReID. This is challenging because it requires the model to continuously adapt to unlabeled data of the target environments. We design an effective scheme for this task, dubbed CLUDA-ReID, where the anti-forgetting is harmoniously coordinated with the adaptation.
  arXiv  Detail & Related papers  (2021-12-13T13:19:45Z)
• Differentiable Spatial Planning using Transformers [87.90709874369192]
  We propose Spatial Planning Transformers (SPT), which given an obstacle map learns to generate actions by planning over long-range spatial dependencies. In the setting where the ground truth map is not known to the agent, we leverage pre-trained SPTs in an end-to-end framework. SPTs outperform prior state-of-the-art differentiable planners across all the setups for both manipulation and navigation tasks.
  arXiv  Detail & Related papers  (2021-12-02T06:48:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.