Are MLMs Trapped in the Visual Room?

TL;DR

This paper questions whether multi-modal large models truly understand visual content by introducing a two-tier evaluation framework and a sarcasm dataset, revealing a gap between perception accuracy and understanding ability.

Contribution

It proposes a novel perception-cognition evaluation framework and a high-quality sarcasm dataset to empirically assess MLMs' understanding beyond surface perception.

Findings

MLMs show high accuracy in visual perception

MLMs have about 17.1% error in sarcasm understanding despite correct perception

Weaknesses identified in context integration, emotional reasoning, and pragmatic inference

Abstract

Can multi-modal large models (MLMs) that can ``see'' an image be said to ``understand'' it? Drawing inspiration from Searle's Chinese Room, we propose the \textbf{Visual Room} argument: a system may process and describe every detail of visual inputs by following algorithmic rules, without genuinely comprehending the underlying intention. This dilemma challenges the prevailing assumption that perceptual mastery implies genuine understanding. In implementation, we introduce a two-tier evaluation framework spanning perception and cognition. The perception component evaluates whether MLMs can accurately capture the surface-level details of visual contents, where the cognitive component examines their ability to infer sarcasm polarity. To support this framework, We further introduce a high-quality multi-modal sarcasm dataset comprising both 924 static images and 100 dynamic videos. All sarcasm labels are annotated by the original authors and verified by independent reviewers to ensure clarity and consistency. We evaluate eight state-of-the-art (SoTA) MLMs. Our results highlight three key findings: (1) MLMs demonstrate high accuracy in visual perception; (2) even with correct perception, MLMs exhibit an average error rate of ~17.1\% in sarcasm understanding, revealing a significant gap between seeing and understanding; (3) this gap stems from weaknesses in context integration, emotional reasoning, and pragmatic inference. This work provides empirical grounding for the proposed Visual Room argument and offers a new evaluation paradigm for MLMs.