Constraint-Aware Route Recommendation from Natural Language via Hierarchical LLM Agents

• URL: http://arxiv.org/abs/2510.06078v1
• Date: Tue, 07 Oct 2025 16:03:57 GMT
• Title: Constraint-Aware Route Recommendation from Natural Language via Hierarchical LLM Agents
• Authors: Tao Zhe, Rui Liu, Fateme Memar, Xiao Luo, Wei Fan, Xinyue Ye, Zhongren Peng, Dongjie Wang,
• Abstract summary: RouteLLM is a hierarchical multi-agent framework that grounds natural-language intents into constraint-aware routes.<n>It first parses user queries into structured intents including POIs, paths, and constraints.<n>A final verifier agent ensures constraint satisfaction and produces the final route with an interpretable rationale.
• Score: 21.473451572179552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Route recommendation aims to provide users with optimal travel plans that satisfy diverse and complex requirements. Classical routing algorithms (e.g., shortest-path and constraint-aware search) are efficient but assume structured inputs and fixed objectives, limiting adaptability to natural-language queries. Recent LLM-based approaches enhance flexibility but struggle with spatial reasoning and the joint modeling of route-level and POI-level preferences. To address these limitations, we propose RouteLLM, a hierarchical multi-agent framework that grounds natural-language intents into constraint-aware routes. It first parses user queries into structured intents including POIs, paths, and constraints. A manager agent then coordinates specialized sub-agents: a constraint agent that resolves and formally check constraints, a POI agent that retrieves and ranks candidate POIs, and a path refinement agent that refines routes via a routing engine with preference-conditioned costs. A final verifier agent ensures constraint satisfaction and produces the final route with an interpretable rationale. This design bridges linguistic flexibility and spatial structure, enabling reasoning over route feasibility and user preferences. Experiments show that our method reliably grounds textual preferences into constraint-aware routes, improving route quality and preference satisfaction over classical methods.

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