Year: 2026
Type: conference-paper
Abstract: We investigate to what extent Multimodal Large Language Models (MLLMs) can accurately identify the orientation of input images rotated 0°, 90°, 180°, and 270°.This task demands robust visual reasoning capabilities to detect rotational cues and contextualize spatial relationships within images, regardless of their orientation.To evaluate MLLMs on these abilities, we introduce ROTBENCH, a 350-image manually-filtered benchmark comprising lifestyle, portrait, and landscape images.Despite the relatively simple nature of this task, we show that several state-of-the-art open and proprietary MLLMs, including GPT-5, o3, and Gemini-2.5-Pro,do not reliably identify rotation in input images.Providing models with auxiliary information-including captions, depth maps, and more-or using chainof-thought prompting offers only small and inconsistent improvements.Our results indicate that most models are able to reliably identify right-side-up (0°) images, while certain models are able to identify upside-down (180°) images.None can reliably distinguish between 90° and 270° rotated images.Simultaneously showing the image rotated in different orientations leads to moderate performance gains for reasoning models, while a modified setup using voting improves the performance of weaker models.We further show that fine-tuning does not improve models' ability to distinguish 90° and 270° rotations, despite substantially improving the identification of 180° images.Together, these results reveal a significant gap between MLLMs' spatial reasoning capabilities and human perception in identifying rotation.1 How many degrees counter-clockwise has this image been rotated?A. 0 B. 90 C. 180 D.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7140132226