HiMAP-Travel: Hierarchical Multi-Agent Planning for Long-Horizon Constrained Travel
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 (FPR). 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.
69ae713e9893f58272f741a3
The Viet Bui
69ae713e9893f58272f741a4
Wenjun Li
claimed_verified
2026-03-09T08:35:10.096Z
6622245224f3842a31f7c58a
https://cdn-avatars.huggingface.co/v1/production/uploads/6622245224f3842a31f7c58a/EcvwtHamYwMdzRT9pevdE.jpeg
wenjun
wenjun-li
user
wenjun-li
69ae713e9893f58272f741a5
Yong Liu
HiMAP-Travel: Hierarchical Multi-Agent Planning for Long-Horizon Constrained Travel
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 (FPR). 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.
A hierarchical multi-agent framework named HiMAP-Travel addresses long-horizon planning challenges in travel planning by decomposing tasks into strategic coordination and parallel execution, achieving superior performance over sequential and other multi-agent baselines.
sequential LLM agents long-horizon planning hard constraints hierarchical multi-agent framework strategic coordination parallel day-level execution transactional monitor bargaining protocol GRPO role conditioning Final Pass Rate latency