TorchUMM: A Unified Multimodal Model Codebase for Evaluation, Analysis, and Post-training
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arXiv
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| Format: | Preprint |
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2026
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| author | Luo, Yinyi Wang, Wenwen Bai, Hayes Zhu, Hongyu Chen, Hao He, Pan Savvides, Marios Li, Sharon Wang, Jindong |
| author_facet | Luo, Yinyi Wang, Wenwen Bai, Hayes Zhu, Hongyu Chen, Hao He, Pan Savvides, Marios Li, Sharon Wang, Jindong |
| contents | Recent advances in unified multimodal models (UMMs) have led to a proliferation of architectures capable of understanding, generating, and editing across visual and textual modalities. However, developing a unified framework for UMMs remains challenging due to the diversity of model architectures and the heterogeneity of training paradigms and implementation details. In this paper, we present TorchUMM, the first unified codebase for comprehensive evaluation, analysis, and post-training across diverse UMM backbones, tasks, and datasets. TorchUMM supports a broad spectrum of models covering a wide range of scales and design paradigms. Our benchmark encompasses three core task dimensions: multimodal understanding, generation, and editing, and integrates both established and novel datasets to evaluate perception, reasoning, compositionality, and instruction-following abilities. By providing a unified interface and standardized evaluation protocols, TorchUMM enables fair and reproducible comparisons across heterogeneous models and fosters deeper insights into their strengths and limitations, facilitating the development of more capable unified multimodal systems. Code is available at: https://github.com/AIFrontierLab/TorchUMM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_10784 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TorchUMM: A Unified Multimodal Model Codebase for Evaluation, Analysis, and Post-training Luo, Yinyi Wang, Wenwen Bai, Hayes Zhu, Hongyu Chen, Hao He, Pan Savvides, Marios Li, Sharon Wang, Jindong Artificial Intelligence Recent advances in unified multimodal models (UMMs) have led to a proliferation of architectures capable of understanding, generating, and editing across visual and textual modalities. However, developing a unified framework for UMMs remains challenging due to the diversity of model architectures and the heterogeneity of training paradigms and implementation details. In this paper, we present TorchUMM, the first unified codebase for comprehensive evaluation, analysis, and post-training across diverse UMM backbones, tasks, and datasets. TorchUMM supports a broad spectrum of models covering a wide range of scales and design paradigms. Our benchmark encompasses three core task dimensions: multimodal understanding, generation, and editing, and integrates both established and novel datasets to evaluate perception, reasoning, compositionality, and instruction-following abilities. By providing a unified interface and standardized evaluation protocols, TorchUMM enables fair and reproducible comparisons across heterogeneous models and fosters deeper insights into their strengths and limitations, facilitating the development of more capable unified multimodal systems. Code is available at: https://github.com/AIFrontierLab/TorchUMM. |
| title | TorchUMM: A Unified Multimodal Model Codebase for Evaluation, Analysis, and Post-training |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2604.10784 |