# RETAIL: Towards Real-world Travel Planning for Large Language Models

arXiv: https://arxiv.org/abs/2508.15335 · HTML: https://mlsanity.com/paper/2508.15335/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 46 references that are in the map.
- First posted on arXiv 21 Aug 2025.

## Builds on (most cited first, 19 of 19)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://mlsanity.com/paper/2406.12793/) (2024)
- [TravelPlanner: A Benchmark for Real-World Planning with Language Agents](https://mlsanity.com/paper/2402.01622/) (2024)
- [Large Language Models for Time Series: A Survey](https://mlsanity.com/paper/2402.01801/) (2024)
- [EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms](https://mlsanity.com/paper/2406.14228/) (2024)
- [Towards Conversational Recommendation over Multi-Type Dialogs](https://mlsanity.com/paper/2005.03954/) (2020)
- [Ask-before-Plan: Proactive Language Agents for Real-World Planning](https://mlsanity.com/paper/2406.12639/) (2024)
- [TravelAgent: An AI Assistant for Personalized Travel Planning](https://mlsanity.com/paper/2409.08069/) (2024)
- [ITINERA: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planning](https://mlsanity.com/paper/2402.07204/) (2024)
- [To the Globe (TTG): Towards Language-Driven Guaranteed Travel Planning](https://mlsanity.com/paper/2410.16456/) (2024)
- [TRIP-PAL: Travel Planning with Guarantees by Combining Large Language Models and Automated Planners](https://mlsanity.com/paper/2406.10196/) (2024)
- [P-RAG: Progressive Retrieval Augmented Generation For Planning on Embodied Everyday Task](https://mlsanity.com/paper/2409.11279/) (2024)
- [MidMed: Towards Mixed-Type Dialogues for Medical Consultation](https://mlsanity.com/paper/2306.02923/) (2023)
- [Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals](https://mlsanity.com/paper/2204.07299/) (2022)
- [Towards Full Delegation: Designing Ideal Agentic Behaviors for Travel Planning](https://mlsanity.com/paper/2411.13904/) (2024)

## Built on by (most cited first, 9 of 9)

- [DeepTravel: An End-to-End Agentic Reinforcement Learning Framework for Autonomous Travel Planning Agents](https://mlsanity.com/paper/2509.21842/) (2025)
- [GroupTravelBench: Benchmarking LLM Agents on Multi-Person Travel Planning](https://mlsanity.com/paper/2605.25200/) (2026)
- [TREK: A Travel Reasoning and Evaluation Kit for LLM Agents in Complex Trip Planning](https://mlsanity.com/paper/2607.26977/) (2026)
- [TRACE: Tourism Recommendation with Accountable Citation Evidence](https://mlsanity.com/paper/2605.07677/) (2026)
- [PlanningBench: Generating Scalable and Verifiable Planning Data for Evaluating and Training Large Language Models](https://mlsanity.com/paper/2605.20873/) (2026)
- [Replan, Repair, or Edit? A Unified Empirical Evaluation of Travel Agents for Itinerary Revision under Resource Disruptions](https://mlsanity.com/paper/2609.19654/) (2026)
- [Revisiting the Travel Planning Capabilities of Large Language Models](https://mlsanity.com/paper/2605.03308/) (2026)
- [Behavior2Trip: Towards Personalized Travel Planning via User Behavior Trajectory](https://mlsanity.com/paper/2608.26807/) (2026)
- [JarvisGUI: Towards Cross-Device GUI Agents with Dynamic Task Composition](https://mlsanity.com/paper/2609.10451/) (2026)

## Abstract (arXiv)

Although large language models have enhanced automated travel planning abilities, current systems remain misaligned with real-world scenarios. First, they assume users provide explicit queries, while in reality requirements are often implicit. Second, existing solutions ignore diverse environmental factors and user preferences, limiting the feasibility of plans. Third, systems can only generate plans with basic POI arrangements, failing to provide all-in-one plans with rich details. To mitigate these challenges, we construct a novel dataset RETAIL, which supports decision-making for implicit queries while covering explicit queries, both with and without revision needs. It also enables environmental awareness to ensure plan feasibility under real-world scenarios, while incorporating detailed POI information for all-in-one travel plans. Furthermore, we propose a topic-guided multi-agent framework, termed TGMA. Our experiments reveal that even the strongest existing model achieves merely a 1.0% pass rate, indicating real-world travel planning remains extremely challenging. In contrast, TGMA demonstrates substantially improved performance 2.72%, offering promising directions for real-world travel planning.