Revisiting the Travel Planning Capabilities of Large Language Models

• URL: http://arxiv.org/abs/2605.03308v1
• Date: Tue, 05 May 2026 02:56:25 GMT
• Title: Revisiting the Travel Planning Capabilities of Large Language Models
• Abstract summary: We decompose travel planning into five constituent atomic sub-capabilities, including emphConstraint Extraction, emphTool Use, emphPlan Generation, emphError Identification, and emphError Correction.<n>Our results highlight a clear contrast in performance: while LLMs are proficient in extracting explicit constraints, they struggle to infer implicit, open-world requirements.
• Score: 22.520715389751828
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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