@inproceedings{niu-etal-2026-understanding,
title = "Does Understanding Inform Generation in Unified Multimodal Models? From Analysis to Path Forward",
author = "Niu, Yuwei and
Jin, Weiyang and
Liao, Jiaqi and
Feng, Chaoran and
Jin, Peng and
Lin, Bin and
Li, Zongjian and
Zhu, Bin and
Yu, Weihao and
Yuan, Li",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.966/",
pages = "22283--22303",
ISBN = "979-8-89176-459-0",
abstract = "Recent years have witnessed significant progress in Unified Multimodal Models, yet a fundamental question remains: Does understanding truly inform generation in Unified Multimodal Models? 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 understanding-generation gap."
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<abstract>Recent years have witnessed significant progress in Unified Multimodal Models, yet a fundamental question remains: Does understanding truly inform generation in Unified Multimodal Models? 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 understanding-generation gap.</abstract>
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%0 Conference Proceedings
%T Does Understanding Inform Generation in Unified Multimodal Models? From Analysis to Path Forward
%A Niu, Yuwei
%A Jin, Weiyang
%A Liao, Jiaqi
%A Feng, Chaoran
%A Jin, Peng
%A Lin, Bin
%A Li, Zongjian
%A Zhu, Bin
%A Yu, Weihao
%A Yuan, Li
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F niu-etal-2026-understanding
%X Recent years have witnessed significant progress in Unified Multimodal Models, yet a fundamental question remains: Does understanding truly inform generation in Unified Multimodal Models? 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 understanding-generation gap.
%U https://aclanthology.org/2026.emnlp-main.966/
%P 22283-22303
Markdown (Informal)
[Does Understanding Inform Generation in Unified Multimodal Models? From Analysis to Path Forward](https://aclanthology.org/2026.emnlp-main.966/) (Niu et al., EMNLP 2026)
ACL
- Yuwei Niu, Weiyang Jin, Jiaqi Liao, Chaoran Feng, Peng Jin, Bin Lin, Zongjian Li, Bin Zhu, Weihao Yu, and Li Yuan. 2026. Does Understanding Inform Generation in Unified Multimodal Models? From Analysis to Path Forward. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 22283–22303, Budapest, Hungary. Association for Computational Linguistics.