Revisiting the Travel Planning Capabilities of Large Language Models

2026-05-04

AI Summary

This work addresses the poor performance of large language models (LLMs) on long-horizon reasoning tasks such as travel planning and the lack of interpretability in existing evaluation methods. The authors propose the first atomic capability decomposition framework tailored to travel planning, breaking the task into five modules: constraint extraction, tool utilization, plan generation, error identification, and correction. They further introduce an oracle-assisted intermediate-state evaluation protocol that isolates component performance to mitigate cascading errors. Experimental results reveal that while LLMs effectively extract explicit constraints, they exhibit systematic deficiencies in reasoning about implicit user needs, ensuring structural coherence of plans, and self-correction—manifesting as excessive sensitivity and error persistence. This approach substantially enhances both the precision and interpretability of model evaluation.

Abstract

Travel planning serves as a critical task for long-horizon reasoning, exposing significant deficits in LLMs. However, existing benchmarks and evaluations primarily assess final plans in an end-to-end manner, which lacks interpretability and makes it difficult to analyze the root causes of failures. To bridge this gap, we decompose travel planning into five constituent atomic sub-capabilities, including \emph{Constraint Extraction}, \emph{Tool Use}, \emph{Plan Generation}, \emph{Error Identification}, and \emph{Error Correction}. We implement a decoupled evaluation protocol leveraging oracle intermediate contexts to rigorously isolate these components, thereby measuring the atomic performance boundary without the noise of cascading errors. Our results highlight a clear contrast in performance: while LLMs are proficient in extracting explicit constraints, they struggle to infer implicit, open-world requirements. Furthermore, they exhibit structural biases in plan generation and suffer from ineffective self-correction, characterized by excessive sensitivity and erroneous persistence. These findings offer precise directions for improving LLM reasoning and planning abilities.

Problem

• travel planning
• large language models
• evaluation benchmark
• long-horizon reasoning
• interpretability

Innovation

• atomic sub-capabilities
• decoupled evaluation
• oracle intermediate contexts
• cascading error isolation
• LLM reasoning analysis