Iti-Validator: A Guardrail Framework for Validating and Correcting LLM-Generated Itineraries
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Abstract (arXiv)
The rapid advancement of Large Language Models (LLMs) has enabled them to generate complex, multi-step plans and itineraries. However, these generated plans often lack temporal and spatial consistency, particularly in scenarios involving physical travel constraints. This research aims to study the temporal performance of different LLMs and presents a validation framework that evaluates and improves the temporal consistency of LLM-generated travel itineraries. The system employs multiple state-of-the-art LLMs to generate travel plans and validates them against real-world flight duration constraints using the AeroDataBox API. This work contributes to the understanding of LLM capabilities in handling complex temporal reasoning tasks like itinerary generation and provides a framework to rectify any temporal inconsistencies like overlapping journeys or unrealistic transit times in the itineraries generated by LLMs before the itinerary is given to the user. Our experiments reveal that while current LLMs frequently produce temporally inconsistent itineraries, these can be systematically and reliably corrected using our framework, enabling their practical deployment in large-scale travel planning.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 14 references that are in the map. First posted on arXiv 4 Sep 2025.
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References · 7
- 2024TravelPlanner: A Benchmark for Real-World Planning with Language Agentscited by 392
- 2024Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoningcited by 88
- 2023Can Large Language Models be Good Path Planners? A Benchmark and Investigation on Spatial-temporal Reasoningcited by 49
- 2024LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path Planningcited by 45
- 2024ITINERA: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planningcited by 33
- 2025TripCraft: A Benchmark for Spatio-Temporally Fine Grained Travel Planningcited by 21
- 2025TCP: a Benchmark for Temporal Constraint-Based Planningcited by 8
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- 2024Robust Planning with LLM-Modulo Framework: Case Study in Travel Planning
- 2026TREK: A Travel Reasoning and Evaluation Kit for LLM Agents in Complex Trip Planning
- 2026TravelEval: A Comprehensive Benchmarking Framework for Evaluating LLM-Powered Travel Planning Agents
- 2026ItinBench: Benchmarking Planning Across Multiple Cognitive Dimensions with Large Language Models
- 2024Benchmarking the Capabilities of Large Language Models in Transportation System Engineering: Accuracy, Consistency, and Reasoning Behaviors
- 2025GridRoute: A Benchmark for LLM-Based Route Planning with Cardinal Movement in Grid Environments