arXiv AI

Boogu-Image-0.1: Boosting Open-Source Unified Multimodal Understanding and Generation

arXiv:2607. 13125v1 Announce Type: cross Abstract: We introduce Boogu-Image-0.

arXiv AI

Jul 21

Boogu-Image-0.1: Boosting Open Agentic Multimodal Generation via Understanding under a Minimal Budget

arXiv:2607. 13125v2 Announce Type: replace-cross Abstract: We introduce Boogu-Image-0.

By Guoxuan Chen, Chufeng Xiao, Haoran Yang, Siyue Xie, Binxiao Huang, Ming Zhang, Cheuk Him Chau, Xinyu Fu, Yingzhao Lian, Tom S. Y. Li, Jintao Lin, Bowen Dong, Zian Qian, Yuhao Liu, Yuxuan Hu, Weikang Shi, Bin Zou, Bowen Zheng, Haoxuan Che, Chang Chen, Yuyang He, Heyang Sun, Tianyu Huang, Chong Hou Choi, Cheng Gong, Han Shi, Haoli Bai, Xihui Liu, Hongsheng Li, Qifeng Chen, Chao Huang, Rui Liu, Chenyang Lei

llms agents diffusion multimodal benchmarks data-engineering

arXiv AI

1d ago

OpenGPT-4o-Image: A Comprehensive Dataset for Advanced Image Generation and Editing

arXiv:2509. 24900v2 Announce Type: replace-cross Abstract: The performance of unified multimodal models for image generation and editing is fundamentally constrained by the quality and comprehensiveness of their training data.

By Zhihong Chen, Xuehai Bai, Yang Shi, Chaoyou Fu, Huanyu Zhang, Haotian Wang, Xiaoyan Sun, Zhang Zhang, Liang Wang, Yuanxing Zhang, Pengfei Wan, Yi-Fan Zhang

llms diffusion robotics fine-tuning multimodal benchmarks

Hugging Face Trending Papers

Jun 11

InterleaveThinker: Reinforcing Agentic Interleaved Generation

Recent image generators have demonstrated impressive photorealism and instruction-following capabilities in single-image generation and editing. However, constrained by their architectures, they cannot achieve interleaved generation (text-image sequence), which has crucial applications in visual narratives, guidance, and embodied manipulation.

llms agents diffusion reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv AI

Jul 29

Argus-Unified: Towards A Compact and Economical Unified Model for Image Understanding and Generation

arXiv:2607. 25527v1 Announce Type: cross Abstract: Unifying visual understanding and generation in one model holds immense promise, but remains challenging and expensive due to heavy compute and data demands and conflicts between the visual features needed for these two capabilities.

By Weiming Zhuang, Jiabo Huang, Jingtao Li, Zhizhong Li, Chen Chen, Sina Sajadmanesh, Lingjuan Lyu

llms multimodal benchmarks

arXiv AI

Jul 22

Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing

arXiv:2607. 19064v1 Announce Type: cross Abstract: Large-scale visual generators are increasingly capable but costly to train, fine-tune, and deploy.

By Xinjie Zhang, Peng Zhang, Shicheng Zheng, Jinghao Guo, Zhaoyang Jia, Yifei Shen, Xun Guo, Yuxuan Luo, Jiahao Li, Wenxuan Xie, Fanyi Pu, Xiaoyi Zhang, Kaichen Zhang, Zongyu Guo, Tianci Bi, Dongnan Gui, Zhening Liu, Zimo Wen, Zihan Zheng, Senqiao Yang, Xiao Li, Jinglu Wang, Bin Li, Yan Lu

llms diffusion nlp reinforcement-learning fine-tuning efficiency multimodal benchmarks safety

Hugging Face Trending Papers

Jul 21

Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing

Large-scale visual generators are increasingly capable but costly to train, fine-tune, and deploy. We introduce Mage-Flow, a compact 4B-scale generative stack for efficient text-to-image generation and instruction-based image editing.

llms diffusion nlp reinforcement-learning fine-tuning efficiency multimodal benchmarks safety

Hugging Face Trending Papers

Jul 27

UniGen-AR: Unifying Visual Generation with Auto-Regressive Modeling

Modern computer vision pipelines remain fragmented, with tasks such as text-to-image generation, editing, restoration, and classical perception handled by separate models. We study Unified Visual Generation (UVG), where a single model produces diverse image-valued outputs through a unified multimodal interface.

llms diffusion computer-vision nlp efficiency multimodal

arXiv AI

Jun 3

Vision Inference Former: Sustaining Visual Consistency in Multimodal Large Language Models

arXiv:2605. 18160v2 Announce Type: replace-cross Abstract: In recent years, multimodal large language models (MLLMs) have achieved remarkable progress, primarily attributed to effective paradigms for integrating visual and textual information.

By Xinpeng Dong, Min Zhang, Kairong Han, Xu Tan, Fei Wu, Kun Kuang

llms computer-vision multimodal benchmarks safety

arXiv AI

Jul 3

MMBench-Live: A Continuously Evolving Benchmark for Multimodal Models

arXiv:2607. 01813v1 Announce Type: cross Abstract: Evaluation benchmarks are essential for assessing vision-language models (VLMs), but most multimodal benchmarks are static, making them vulnerable to temporal staleness, data contamination, and costly maintenance.

By Yuanzhi Liu, Shousheng Zhao, Bo Zhou, Kongming Liang, Zhanyu Ma

llms agents multimodal benchmarks safety

Hugging Face Trending Papers

Jun 23

S1-Omni-Image: A Unified Model for Scientific Image Understanding, Generation, and Editing

We present S1-Omni-Image, an open-weight unified multimodal model for scientific image understanding, generation, and editing. Unlike general-purpose image generation models, scientific image tasks require not only high-fidelity synthesis, but also robust understanding of scientific semantics, structural relations, domain knowledge, and task intent.

diffusion computer-vision multimodal benchmarks

Hugging Face Trending Papers

Aug 3

MIEScore: Human-Aligned Evaluation for Multi-Source Image Editing

Recent advances in unified multimodal models have significantly improved text-guided image editing abilities. In particular, models such as Nano-Banana-Pro and GPT-Image-2 demonstrate emerging capabilities in multi-source image editing (MIE), including tasks such as object synthesis, person-background composition, and cross-image style fusion.

llms fine-tuning multimodal benchmarks

arXiv AI

Jun 26

Reconstruction Alignment Improves Unified Multimodal Models

arXiv:2509. 07295v4 Announce Type: replace-cross Abstract: Unified multimodal models (UMMs) unify visual understanding and generation within a single architecture.

By Ji Xie, Trevor Darrell, Luke Zettlemoyer, XuDong Wang

rag diffusion multimodal benchmarks safety

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