TravelEval: A Comprehensive Benchmarking Framework for Evaluating LLM-Powered Travel Planning Agents
- URL: http://arxiv.org/abs/2606.01046v1
- Date: Sun, 31 May 2026 06:29:19 GMT
- Title: TravelEval: A Comprehensive Benchmarking Framework for Evaluating LLM-Powered Travel Planning Agents
- Abstract summary: We introduce TravelEval, a realistic and comprehensive benchmark for evaluating large language travel models.<n>TravelEval 1) a novel six-dimensional evaluation framework to holistically assess plans across accuracy, compliance, temporality, spatiality, economy, and utility dimensions.
- Score: 16.732203115366584
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The development of Large Language Models (LLMs) has significantly improved travel planning applications, yet evaluating such models is limited by existing benchmarks' limitations: 1) overemphasis on constraint compliance, neglecting multi-dimensional qualities like spatio-temporal cost; 2) datasets lacking real-world authenticity and coverage in key areas (e.g., lodging, transport); and 3) isolated daily plan assessments that miss critical details (e.g., the impact of daily accommodation and visit pacing) needed for entire plan's evaluation. To address this gap, we introduce TravelEval, a realistic and comprehensive benchmark. TravelEval features 1) a novel six-dimensional evaluation framework to holistically assess plans across accuracy, compliance, temporality, spatiality, economy, and utility dimensions; 2) a highly realistic data sandbox with precise accommodation pricing and authentic intercity transportation data; and 3) a simulation-based global evaluation method that emulates complete travel plans with API-integrated geographic information and fine-grained queuing time. Evaluating 12 mainstream approaches with TravelEval reveals several valuable insights, such that LLMs struggle with globally-optimized multi-dimensional planning (especially in spatio-temporal reasoning and budget compliance), and agentic reasoning strategies offer no consistent improvement. Concisely, TravelEval facilitates travel plan evaluation via grounded spatio-temporal emulation and comprehensive metrics, providing a robust foundation for advancing LLM-powered travel planning research and applications.
Related papers
- TRIPPULSE: Multi-Agent Travel Planning with Review-Grounded Reasoning [8.15237621583531]
We propose TRIPPULSE, a multi- agent framework for review-grounded travel planning.<n>We augment TRIPCRAFT with 100K+ real-world reviews and introduce Review-Grounded Per- sona Alignment (RGPA), an LLM-as-a-Judge metric for evaluating alignment with human-centric travel experiences.
arXiv Detail & Related papers (2026-08-31T15:02:37Z) - TripScore: Benchmarking and rewarding real-world travel planning with fine-grained evaluation [4.831964966659024]
We introduce a comprehensive benchmark for travel planning that unifies fine-grained criteria into a single reward.<n>Our evaluator achieves moderate agreement with travel-expert annotations (60.75%)<n>We release a large-scale dataset of 4,870 queries including 219 real-world, free-form requests for generalization to authentic user intent.
arXiv Detail & Related papers (2025-10-10T05:22:29Z) - 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) - 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) - Wide-Horizon Thinking and Simulation-Based Evaluation for Real-World LLM Planning with Multifaceted Constraints [39.01715254437105]
This paper introduces the Multiple Aspects of Planning (MAoP) to solve planning problems with multifaceted constraints.<n>Instead of direct planning, MAoP leverages the strategist to conduct pre-planning from various aspects and provide the planning blueprint for planners.
arXiv Detail & Related papers (2025-06-14T09:37:59Z) - 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) - ChinaTravel: An Open-Ended Benchmark for Language Agents in Chinese Travel Planning [38.44879526364259]
We introduce emphChinaTravel, the first open-ended benchmark grounded in authentic Chinese travel requirements.<n>We design a compositionally generalizable domain-specific language for scalable evaluation, covering feasibility, constraint satisfaction, and preference comparison.<n> Empirical studies reveal the potential of neuro-symbolic agents in travel planning, achieving a 37.0% constraint satisfaction rate on human queries.
arXiv Detail & Related papers (2024-12-18T10:10:12Z) - EgoPlan-Bench2: A Benchmark for Multimodal Large Language Model Planning in Real-World Scenarios [53.26658545922884]
We introduce EgoPlan-Bench2, a benchmark designed to assess the planning capabilities of MLLMs across a wide range of real-world scenarios.<n>We evaluate 21 competitive MLLMs and provide an in-depth analysis of their limitations, revealing that they face significant challenges in real-world planning.<n>Our approach enhances the performance of GPT-4V by 10.24 on EgoPlan-Bench2 without additional training.
arXiv Detail & Related papers (2024-12-05T18:57:23Z) - On The Planning Abilities of OpenAI's o1 Models: Feasibility, Optimality, and Generalizability [59.72892401927283]
We evaluate the planning capabilities of OpenAI's o1 models across a variety of benchmark tasks.
Our results reveal that o1-preview outperforms GPT-4 in adhering to task constraints.
arXiv Detail & Related papers (2024-09-30T03:58:43Z) - NATURAL PLAN: Benchmarking LLMs on Natural Language Planning [109.73382347588417]
We introduce NATURAL PLAN, a realistic planning benchmark in natural language containing 3 key tasks: Trip Planning, Meeting Planning, and Calendar Scheduling.
We focus our evaluation on the planning capabilities of LLMs with full information on the task, by providing outputs from tools such as Google Flights, Google Maps, and Google Calendar as contexts to the models.
arXiv Detail & Related papers (2024-06-06T21:27:35Z) - TravelPlanner: A Benchmark for Real-World Planning with Language Agents [63.199454024966506]
We propose TravelPlanner, a new planning benchmark that focuses on travel planning, a common real-world planning scenario.
It provides a rich sandbox environment, various tools for accessing nearly four million data records, and 1,225 meticulously curated planning intents and reference plans.
Comprehensive evaluations show that the current language agents are not yet capable of handling such complex planning tasks-even GPT-4 only achieves a success rate of 0.6%.
arXiv Detail & Related papers (2024-02-02T18:39:51Z) - Plan2Vec: Unsupervised Representation Learning by Latent Plans [106.37274654231659]
We introduce plan2vec, an unsupervised representation learning approach that is inspired by reinforcement learning.
Plan2vec constructs a weighted graph on an image dataset using near-neighbor distances, and then extrapolates this local metric to a global embedding by distilling path-integral over planned path.
We demonstrate the effectiveness of plan2vec on one simulated and two challenging real-world image datasets.
arXiv Detail & Related papers (2020-05-07T17:52:23Z)
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.