Large Vision--Language Models (LVLMs) have achieved remarkable proficiency in explicit visual recognition, effectively describing what is directly visible in an image. However, a critical cognitive gap emerges when the visual input serves only as a clue rather than the answer. We identify that current models struggle with the complex, multi-step reasoning required to solve problems where information is not explicitly depicted. Successfully solving a rebus puzzle requires a distinct cognitive workflow: the model must extract visual and textual attributes, retrieve linguistic prior knowledge (such as idioms), and perform abstract mapping to synthesize these elements into a meaning that exists outside the pixel space. To evaluate this neurosymbolic capability, we introduce RebusBench, a benchmark of 1,164 puzzles designed to test this specific integration of perception and knowledge. Our evaluation of state-of-the-art models (including Qwen, InternVL, and LLaVA) shows a severe deficiency: performance saturates below 10% Exact Match and 20% semantic accuracy, with no significant improvement observed from model scaling or In-Context Learning (ICL). These findings suggest that while models possess the necessary visual and linguistic components, they lack the cognitive reasoning glue to connect them.
@inproceedings{kasaei2026rebusbench,
title={Hidden Meanings in Plain Sight: RebusBench for Evaluating Cognitive Visual Reasoning},
author={Seyed Amir Kasaei and Arash Marioriyad and Mahbod Khaleti and MohammadAmin Fazli and Mahdieh Soleymani Baghshah and Mohammad Hossein Rohban},
booktitle={ICLR 2026 Workshop - From Human Cognition to AI Reasoning: Models, Methods, and Applications},
year={2026},
url={https://openreview.net/forum?id=LCc2CP4aS4}
}