Does Understanding Inform Generation in Unified Multimodal Models? From Analysis to Path Forward
Yuwei Niu, Weiyang Jin, Jiaqi Liao, Chaoran Feng, Peng Jin, Bin Lin, Zongjian Li, Bin Zhu, Weihao Yu, Li Yuan
2025-11-26
Summary
This paper investigates whether multimodal AI models that can 'understand' information also actually use that understanding when they 'generate' responses, like text or images. They created a new way to test this, called UniSandbox, using specifically designed datasets.
What's the problem?
Current AI models are getting good at both understanding information from different sources (like images and text) and generating new content. However, it's unclear if the understanding part actually *helps* the generation part, or if the model is just good at both things independently. There's a question of whether the model is truly reasoning and applying knowledge, or just mimicking patterns it's seen in training data.
What's the solution?
The researchers built UniSandbox, a testing environment that lets them carefully control the data and avoid accidental 'cheating' where the model might already know the answer. They focused on two key areas: reasoning (like solving a problem step-by-step) and knowledge transfer (using new information learned in one task to help with another). They found that using a technique called 'Chain-of-Thought' – where the model explicitly shows its reasoning – improved both understanding and generation. They also showed that the model could learn to reason implicitly through a self-training process. Furthermore, they discovered that certain model designs already have some built-in reasoning abilities that help with knowledge transfer.
Why it matters?
This research is important because it helps us understand how to build better AI models. By identifying the gap between understanding and generation, and showing how techniques like Chain-of-Thought can help bridge that gap, it provides guidance for designing future AI systems that are more intelligent and capable of truly reasoning and applying knowledge.
Abstract
Recent years have witnessed significant progress in Unified Multimodal Models, yet a fundamental question remains: Does understanding truly inform generation? To investigate this, we introduce UniSandbox, a decoupled evaluation framework paired with controlled, synthetic datasets to avoid data leakage and enable detailed analysis. Our findings reveal a significant understanding-generation gap, which is mainly reflected in two key dimensions: reasoning generation and knowledge transfer. Specifically, for reasoning generation tasks, we observe that explicit Chain-of-Thought (CoT) in the understanding module effectively bridges the gap, and further demonstrate that a self-training approach can successfully internalize this ability, enabling implicit reasoning during generation. Additionally, for knowledge transfer tasks, we find that CoT assists the generative process by helping retrieve newly learned knowledge, and also discover that query-based architectures inherently exhibit latent CoT-like properties that affect this transfer. UniSandbox provides preliminary insights for designing future unified architectures and training strategies that truly bridge the gap between understanding and generation. Code and data are available at https://github.com/PKU-YuanGroup/UniSandBox