UniG2U-Bench: Do Unified Models Advance Multimodal Understanding?

2026-03-03

AI Summary

Existing multimodal benchmarks lack a systematic evaluation of how generative capabilities facilitate understanding. This work proposes UniG2U-Bench, the first unified evaluation framework encompassing seven mechanistic categories and thirty subtasks, which enables fine-grained analysis of the role of generation in multimodal understanding through comparative assessment between Generate-then-Answer and direct reasoning paradigms. The study reveals that unified models generally underperform base vision-language models, and generation typically degrades performance; however, it significantly enhances results on tasks involving spatial perception, visual illusions, and multi-step image-state reasoning. These findings uncover inductive biases rooted in the alignment among task structure, data, and model architecture. This work establishes the first systematic benchmark for evaluating generation-driven understanding mechanisms in multimodal settings.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Unified multimodal models have recently demonstrated strong generative capabilities, yet whether and when generation improves understanding remains unclear. Existing benchmarks lack a systematic exploration of the specific tasks where generation facilitates understanding. To this end, we introduce UniG2U-Bench, a comprehensive benchmark categorizing generation-to-understanding (G2U) evaluation into 7 regimes and 30 subtasks, requiring varying degrees of implicit or explicit visual transformations. Extensive evaluation of over 30 models reveals three core findings: 1) Unified models generally underperform their base Vision-Language Models (VLMs), and Generate-then-Answer (GtA) inference typically degrades performance relative to direct inference. 2) Consistent enhancements emerge in spatial intelligence, visual illusions, or multi-round reasoning subtasks, where enhanced spatial and shape perception, as well as multi-step intermediate image states, prove beneficial. 3) Tasks with similar reasoning structures and models sharing architectures exhibit correlated behaviors, suggesting that generation-understanding coupling induces class-consistent inductive biases over tasks, pretraining data, and model architectures. These findings highlight the necessity for more diverse training data and novel paradigms to fully unlock the potential of unified multimodal modeling.

Problem

• multimodal understanding
• generation-to-understanding
• unified models
• vision-language models
• benchmark

Innovation

• Unified Multimodal Models
• Generation-to-Understanding
• UniG2U-Bench
• Visual Reasoning
• Inductive Bias