The rapid progress of large language models has led to impressive results in a wide array of tasks. However, there remains a need for increasingly challenging datasets to evaluate these models' ability to handle long-range dependencies. In this paper, we present Pathfinder-X2, a novel dataset that builds upon the Pathfinder and Pathfinder-X datasets. Pathfinder-X2 comprises 512x512 pixel images, designed to test large language models' capacity to segment a specific white line dash "snake" with a circle at its tip among a collection of similar, distractor snakes. The increased image resolution and complexity of Pathfinder-X2 present a substantially more challenging task for large language models, contributing to the ongoing development and assessment of such models.
