Revisiting the Travel Planning Capabilities of Large Language Models
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 performa...
| Authors | Bo-Wen Zhang, Jin Ye, Peng-Yu Hua et al. |
|---|---|
| Primary category | cs.AI |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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