Papers with travel planning
Unlocking the Planning Capabilities of Large Language Models with Maximum Diversity Fine-tuning (2025.findings-naacl)
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| Challenge: | Existing studies have shown that LLMs struggle to generate valid plans in the automated planning domain due to weak System 2 competencies. |
| Approach: | They propose a method which uses a random sampling method to select diverse and representative data to enhance sample efficiency and the model’s generalization capability. |
| Outcome: | The proposed method outperforms baseline methods across scales and multiple benchmark domains. |
Subgoal Discovery for Hierarchical Dialogue Policy Learning (D18-1)
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| Challenge: | Existing methods to develop dialogue agents for complex tasks require sparse reward signals. |
| Approach: | They propose a divide-and-conquer approach that exploits the hidden structure of a task . they use subgoals to divide a goal-oriented task into simpler subgoal sets . |
| Outcome: | The proposed approach performs competitively against state-of-the-art methods that require human-defined subgoals. |
Progra: Progress-Aware Reinforcement Learning for Multi-Turn Function Calling (2026.findings-acl)
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Huacan Chai, Zijie Cao, Maolin Ran, Yingxuan Yang, Jianghao Lin, Xin Peng, Hairui Wang, Renjie Ding, Ziyu Wan, Muning Wen, Weiwen Liu, Weinan Zhang, Fei Huang, Ying Wen
| Challenge: | Existing methods for multi-turn function calling are limited by redundancy and lack explicit integration of progress awareness into training. |
| Approach: | They propose a framework that explicitly integrates progress awareness into LLM training for multi-turn function calling. |
| Outcome: | Empirical results show that Progra outperforms existing methods on two public benchmarks. |
TripTailor: A Real-World Benchmark for Personalized Travel Planning (2025.findings-acl)
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| Challenge: | Existing evaluation metrics for travel planning rely on unrealistic simulated data . fewer than 10% of the itineraries generated by the latest state-of-the-art LLMs achieve human-level performance. |
| Approach: | They propose a benchmark for personalized travel planning in real-world scenarios . they identify several critical challenges in travel planning including feasibility and rationality . |
| Outcome: | The proposed benchmarks show that fewer than 10% of the itineraries generated by the latest state-of-the-art LLMs achieve human-level performance. |
Graph-Structured Trajectory Extraction from Travelogues (2025.acl-long)
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Aitaro Yamamoto, Hiroyuki Otomo, Hiroki Ouchi, Shohei Higashiyama, Hiroki Teranishi, Hiroyuki Shindo, Taro Watanabe
| Challenge: | Existing studies treat travelogues as sequences of visited locations, but they lack a benchmark dataset. |
| Approach: | They propose to represent the trajectory as a graph that can capture the hierarchy as well as the visiting order and construct a benchmark dataset for the extraction. |
| Outcome: | The proposed dataset shows that even naive baseline systems can predict visited locations and the visiting order between them, while it is more challenging to predict the hierarchical relations. |
TripCraft: A Benchmark for Spatio-Temporally Fine Grained Travel Planning (2025.acl-long)
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Soumyabrata Chaudhuri, Pranav Purkar, Ritwik Raghav, Shubhojit Mallick, Manish Gupta, Abhik Jana, Shreya Ghosh
| Challenge: | Existing benchmarks such as TravelPlanner and TravelPlann+ rely on semi-synthetic data and lack key real-world constraints. |
| Approach: | They propose a spatio-temporally coherent travel planning dataset incorporating real-world constraints, including public transit schedules, public events, varied attraction categories, and user personas for enhanced personalization. |
| Outcome: | The proposed dataset significantly improves meal scheduling, improving performance from 61% to 80% in the 7-day scenario. |
PlanGenLLMs: A Modern Survey of LLM Planning Capabilities (2025.acl-long)
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| Challenge: | Existing studies have focused on developing LLMs to automate complex planning tasks. |
| Approach: | They propose to provide a comprehensive overview of current LLM planners to fill this gap . they examine performance criteria including completeness, executability, optimality, representation, generalization, and efficiency . |
| Outcome: | The proposed survey examines performance criteria for LLM planners and highlights their strengths and weaknesses. |
Personal Travel Solver: A Preference-Driven LLM-Solver System for Travel Planning (2025.acl-long)
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| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding complex instructions and reasoning across diverse domains. |
| Approach: | They propose to integrate user’s implicit preference into the progress of travel planning by integrating real user reviews and point-of-interest metadata from Google Local into RealTravel. |
| Outcome: | The proposed system achieves better performance than baseline methods and improves the level of personalization. |
Beyond Itinerary Planning—A Real-World Benchmark for Multi-Turn and Tool-Using Travel Tasks (2026.acl-long)
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| Challenge: | Existing studies on LLM performance on travel planning have shown that existing settings are limited due to limited domain coverage, insufficient modeling of users’ implicit preferences in multi-turn conversations, and a lack of evaluation of agents’ capability boundaries. |
| Approach: | They propose a benchmark to evaluate LLMs' planning and tool-use abilities in real-world settings by collecting user queries, user preferences, and tools from real scenarios. |
| Outcome: | The proposed benchmark evaluates agents' capabilities in real-world settings and shows that even advanced models exhibit imbalanced performance across different capabilities. |