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

Tao Zhe, Rui Liu, Fateme Memar, Xiao Luo, Wei Fan, Xinyue Ye, Zhongren Peng, Dongjie Wang

TL;DR

Experiments show that the proposed RouteLLM method reliably grounds textual preferences into constraint-aware routes, improving route quality and preference satisfaction over classical methods.

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.

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

TL;DR

Experiments show that the proposed RouteLLM method reliably grounds textual preferences into constraint-aware routes, improving route quality and preference satisfaction over classical methods.

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.

Paper Structure

This paper contains 28 sections, 3 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Route Recommendation
  4. LLMs for Spatial and Route Reasoning
  5. Methodology
  6. Parsing and Decomposing User Intent into a Computable Route Plan
  7. Mitigating LLM Spatial Weaknesses via Multi-Agent Coordination
  8. Hierarchical Planning and Decomposition
  9. Specialized Sub-Task Execution
  10. POI Selection and Filtering.
  11. Multi-Objective Path Optimization.
  12. Handling Dynamic and Special Constraints
  13. Solution Synthesis and Presentation
  14. Adaptive Multi-Objective Path Planning
  15. Experiment
  16. ...and 13 more sections

Figures (4)

  • Figure 1: RouteLLM introduces a hierarchical multi-agent framework that bridges LLM reasoning with classical routing, enabling flexible, interpretable, and constraint-aware route recommendation.
  • Figure 2: The operational workflow of the RouteLLM framework. A user's natural language request is first parsed into structured requirements. Then, a manager agent coordinates specialized agents (POI, Path, Constraint Agent) to resolve subtasks. Finally, the system delivers an optimized route with detailed reasoning.
  • Figure 3: SP1: Path generated from the original; SP2: Same path overlaid on the toll cost heatmap; SP3: Path generated after modifying the query to prioritize scenic value, shown on the scenic value heatmap; SP4: Scenic-prioritized path overlaid on the toll cost heatmap.
  • Figure 4: Case study on NYC Greenwich Village. Pink lines show generated routes, bubbles mark POIs and start/end points, green lines indicate high-scenic cost streets. (a) R1: Route after changing start point; (b) R2: Route prioritizing shorter paths; (c) R3: Route prioritizing scenic views.