Understanding misalignments in human task-solving trajectories is critical for improving AI models trained to mimic human reasoning. This study categorizes such misalignments into three types: \textbf{(1) Lack of functions to express intent}, \textbf{(2) Inefficient action sequences}, and \textbf{(3) Incorrect intentions that cannot solve the task}. To address these issues, we first formalize and define these three types of misalignments. We then propose a heuristic algorithm to detect these misalignments in O2ARC trajectories and conduct a hierarchical and quantitative analysis of their impact. Furthermore, we introduce an intention estimation algorithm that predicts missing alignment information between user actions and inferred intentions, leveraging our formalized framework. Through trajectory alignment, we experimentally demonstrate that AI models trained on human task-solving trajectories improve performance in mimicking human reasoning. Based on hierarchical analysis and experiments, we highlight the importance of trajectory-intention alignment and demonstrate the potential of intention learning.
View on arXiv@article{kim2025_2409.14191,
title={ Addressing and Visualizing Misalignments in Human Task-Solving Trajectories },
author={ Sejin Kim and Hosung Lee and Sundong Kim },
journal={arXiv preprint arXiv:2409.14191},
year={ 2025 }
}