Technology Category
Planning, Routing, and Scheduling: Planning with Language ModelsSearch and Optimization: Learning to SearchMultiagent Systems: Multiagent Planning
Constraint-Aware Route Recommendation from Natural Language via Hierarchical LLM Agents
🤖 AI Summary
Existing path recommendation methods struggle to jointly model natural language query understanding, spatial structural reasoning, and path- and POI-level preferences. Traditional routing algorithms lack semantic flexibility, while pure large language model (LLM) approaches underperform in satisfying spatial constraints and capturing fine-grained user preferences. To address these limitations, we propose a hierarchical multi-agent LLM framework that decomposes the task into four coordinated sub-agents: intent parsing, constraint verification, POI ranking, and path optimization. The framework integrates LLM-based semantic comprehension, logical constraint checking, a geographic routing engine, and preference-conditioned cost modeling. It enables interpretable, end-to-end path generation while strictly adhering to spatiotemporal constraints and preserving semantic fidelity. Experimental results demonstrate that our method significantly outperforms baseline approaches in both path quality and preference satisfaction, achieving high-accuracy, interpretable, and constraint-compliant path recommendation driven by natural language queries.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Grounding natural language queries into constraint-aware route recommendations
Bridging linguistic flexibility with spatial reasoning for route planning
Joint modeling of route-level and POI-level preferences from text
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hierarchical multi-agent framework grounds natural language intents
Specialized agents parse queries and resolve spatial constraints
Routing engine refines routes with preference-conditioned costs
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