Can Unified Generation and Understanding Models Maintain Semantic Equivalence Across Different Output Modalities?

TL;DR

This paper investigates whether unified multimodal large language models can maintain semantic consistency across text and image outputs, revealing significant challenges in cross-modal semantic alignment despite strong individual modality performance.

Contribution

The study introduces VGUBench, a diagnostic framework to evaluate reasoning and rendering across modalities, exposing a gap in semantic alignment in current U-MLLMs.

Findings

Models perform well in textual reasoning and visual rendering.

Models fail to maintain semantic equivalence in visual responses.

Performance in visual answering is weakly correlated with rendering quality.

Abstract

Unified Multimodal Large Language Models (U-MLLMs) integrate understanding and generation within a single architecture. However, existing evaluations typically assess these capabilities separately, overlooking semantic equivalence, i.e., the ability to manifest consistent reasoning results regardless of the output modality. In this work, we investigate whether current U-MLLMs satisfy this premise. We observe that while models demonstrate robust textual reasoning, they fail to maintain semantic equivalence when required to render the same results in the image modality. To rigorously diagnose this discrepancy, we introduce VGUBench, a framework to decouple reasoning logic from generation fidelity. VGUBench comprises three diagnostic tasks: (1)Textual Generative Understanding, establishing a baseline for reasoning accuracy in textual response; (2)Visual Generative Understanding, evaluating the ability to generate visual responses that represent the correct answer; and (3)a Visual Rendering control task, which assesses the ability to directly render explicit visual descriptions into images without complex reasoning. Our evaluation reveals a significant disparity: despite strong performance in textual understanding and visual rendering, U-MLLMs exhibit a marked performance collapse when required to generate visual answers to questions. Furthermore, we find a negligible correlation between visual answering performance and basic rendering quality. These results suggest that the failure stems not from insufficient generation fidelity, but from a breakdown in cross-modal semantic alignment. We provide diagnostic insights to address this challenge in future Unified Generation and Understanding Models.