Does Understanding Inform Generation in Unified Multimodal Models? From Analysis to Path Forward
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
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Yuwei Niu
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Weiyang Jin
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2025-11-27T09:59:57.589Z
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Weiyang Jin
Wayne-King
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Jiaqi Liao
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Chaoran Feng
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2026-04-15T12:10:39.946Z
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Feng Chaoran
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Falcary
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Peng Jin
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Bin Lin
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Zongjian Li
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2026-01-27T14:27:20.223Z
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Zongjian Li
chestnutlzj
user
chestnutlzj
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Bin Zhu
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Weihao Yu
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Li Yuan
Does Understanding Inform Generation in Unified Multimodal Models? From Analysis to Path Forward
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
UniSandbox evaluates Unified Multimodal Models, revealing a gap between understanding and generation, and identifies Chain-of-Thought and self-training as means to bridge this gap.
Unified Multimodal Models UniSandbox decoupled evaluation framework synthetic datasets understanding-generation gap reasoning generation knowledge transfer Chain-of-Thought self-training query-based architectures