HiMAP-Travel: Hierarchical Multi-Agent Planning for Long-Horizon Constrained Travel
#HiMAP-Travel #multi-agent planning #hierarchical planning #long-horizon travel #constrained travel #artificial intelligence #travel optimization
📌 Key Takeaways
- HiMAP-Travel is a hierarchical multi-agent planning system designed for complex, long-term travel planning.
- The system addresses constrained travel scenarios, likely involving multiple objectives or limitations.
- It utilizes a multi-agent approach, suggesting distributed problem-solving among specialized agents.
- The hierarchical structure implies organized, high-level strategy guiding detailed agent actions.
📖 Full Retelling
arXiv:2603.04750v1 Announce Type: new
Abstract: Sequential LLM agents fail on long-horizon planning with hard constraints like budgets and diversity requirements. As planning progresses and context grows, these agents drift from global constraints. We propose HiMAP-Travel, a hierarchical multi-agent framework that splits planning into strategic coordination and parallel day-level execution. A Coordinator allocates resources across days, while Day Executors plan independently in parallel. Three
🏷️ Themes
AI Planning, Travel Technology
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--> Computer Science > Artificial Intelligence arXiv:2603.04750 [Submitted on 5 Mar 2026] Title: HiMAP-Travel: Hierarchical Multi-Agent Planning for Long-Horizon Constrained Travel Authors: The Viet Bui , Wenjun Li , Yong Liu View a PDF of the paper titled HiMAP-Travel: Hierarchical Multi-Agent Planning for Long-Horizon Constrained Travel, by The Viet Bui and 2 other authors View PDF HTML Abstract: Sequential LLM agents fail on long-horizon planning with hard constraints like budgets and diversity requirements. As planning progresses and context grows, these agents drift from global constraints. We propose HiMAP-Travel, a hierarchical multi-agent framework that splits planning into strategic coordination and parallel day-level execution. A Coordinator allocates resources across days, while Day Executors plan independently in parallel. Three key mechanisms enable this: a transactional monitor enforcing budget and uniqueness constraints across parallel agents, a bargaining protocol allowing agents to reject infeasible sub-goals and trigger re-planning, and a single policy trained with GRPO that powers all agents through role conditioning. On TravelPlanner, HiMAP-Travel with Qwen3-8B achieves 52.78% validation and 52.65% test Final Pass Rate . In a controlled comparison with identical model, training, and tools, it outperforms the sequential DeepTravel baseline by +8.67~pp. It also surpasses ATLAS by +17.65~pp and MTP by +10.0~pp. On FlexTravelBench multi-turn scenarios, it achieves 44.34% (2-turn) and 37.42% (3-turn) FPR while reducing latency 2.5x through parallelization. Comments: 33 pages, v1 Subjects: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL) Cite as: arXiv:2603.04750 [cs.AI] (or arXiv:2603.04750v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2603.04750 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Wenjun Li [ view email ] [v1] Thu, 5 Mar 2026 02:55:53 UTC (316 KB) Full-text l...
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