The paper introduces a post‑training approach that enables a single inference process to transition from text reasoning to image synthesis, eliminating the need for explicit modality switching. Using the 14B BAGEL model, the authors demonstrate that targeted post‑training data and reward‑weighted training improve multimodal image generation across four independent T2I benchmarks. The study highlights the benefits of joint text‑image generation and strategic data selection for enhancing T2I performance.
By Jiahui Chen, Philippe Hansen-Estruch, Xiaochuang Han, Yushi Hu, Emily Dinan, Amita Kamath, Michal Drozdzal, Reyhane Askari-Hemmat, Luke Zettlemoyer, Marjan Ghazvininejad
arXiv:2608. 11907v2 Announce Type: replace-cross Abstract: As Large Vision-Language Models increasingly aim to integrate visual generation and understanding within a single parameter space, evaluating such structural unification in a cohesive manner remains a critical challenge.
By Hao Zhang, Jiaxin Qi, Zhijiang Tang, Jianqiang Huang
arXiv:2608. 11907v1 Announce Type: cross Abstract: As Large Vision-Language Models increasingly aim to integrate visual generation and understanding within a single parameter space, evaluating such structural unification in a cohesive manner remains a critical challenge.
By Hao Zhang, Jiaxin Qi, Zhijiang Tang, Jianqiang Huang
arXiv:2608. 08676v1 Announce Type: cross Abstract: Semantic vision encoders have become a central visual interface for multimodal understanding and semantic conditioning in image generation.
By Jinbo Yan, Limeng Qiao, Jie Qin, Junyan He, Feize Wu, Guanglu Wan
As Large Vision-Language Models increasingly aim to integrate visual generation and understanding within a single parameter space, evaluating such structural unification in a cohesive manner remains a critical challenge. Current evaluation protocols predominantly treat generative and discriminative capabilities as separate tasks, leaving a gap in system-level evaluation for unified multimodal models (UMMs).
arXiv:2609.36638v1 Announce Type: new
Abstract: Text-to-image users often provide concise and underspecified prompts, whereas generative models benefit from detailed textual conditions for reliable i...
By Mingfeng Lin, Chengfei Cai, Lin Xu, Chengqian Ma, Yuxiang Wei, Liang Han
arXiv:2610.10400v1 Announce Type: new
Abstract: Multimodal large language models (MLLMs) have made significant progress in visual understanding and generation. However, generating interleaved image--...
By Xin You, Zhiwei Ning, Zukai Chen, Minghui Zhang, Xuanke Shi, Hanxiao Zhang, Jingsong Liu, Jie Yang, Quan Wang, Yun Gu
The paper introduces Modality Self-Play (MSP), a training framework that enables multimodal large language models to learn cross‑modal interactions without requiring paired non‑text data. MSP uses proxy‑mediated textual descriptions of unseen modalities as semantic bridges, allowing a model to generate and align these proxies with observed modalities. Experiments show that MSP achieves state‑of‑the‑art or competitive performance on several multimodal benchmarks and demonstrates effective zero‑shot modality composition through learned bridges and anchor‑supporting proxy descriptions.
By Robert Moseley, Md Kaykobad Reza, Ameya Patil, Edward Ayrapetian, Salman Asif
BiMoGen introduces a unified masked discrete diffusion framework for bidirectional motion‑text generation, addressing limitations of autoregressive models by allowing iterative bidirectional prediction. The method employs a two‑stage training strategy—Decoupled Uni‑ and Cross‑Modal Training followed by supervised fine‑tuning—to establish cross‑modal correspondence and specialize for bidirectional generation. To mitigate train‑test mismatch, Generation‑Aware Self‑Correction exposes the model to its own predictions during training and applies correction passes early in sampling to refine unmasked tokens, achieving competitive results on HumanML3D and KIT‑ML.
By Wanjiang Weng, Yongliang Wu, Xiaofeng Tan, Xingyu Zhu, Wenbo Zhu, Hongsong Wang
arXiv:2607.18227v2 Announce Type: replace
Abstract: In line with the prevailing direction of vision research, we explore the integration of both generation and editing capabilities for video and imag...
By Dingyun Zhang, Lixue Gong, Wei Liu
BiMoGen introduces a unified masked discrete diffusion framework for bidirectional motion‑text generation, addressing the limitations of autoregressive models in capturing bidirectional dependencies between language and motion. The approach employs a two‑stage training strategy—decoupled uni‑ and cross‑modal pretraining followed by supervised fine‑tuning—to establish robust cross‑modal correspondence, and incorporates Generation‑Aware Self‑Correction to mitigate error propagation during inference. Experiments on HumanML3D and KIT‑ML show competitive performance on both text‑to‑motion and motion‑to‑text tasks, demonstrating the effectiveness of the proposed training and correction mechanisms.
SenseNova-U1.5 is an 8B‑MoT native unified multimodal model that can understand, reason about, and generate visual content without using an encoder or VAE. It improves visual fidelity and text rendering through spatially coherent patch reconstruction, large‑scale training on curated generation and editing data, and native resolutions up to 4K. Post‑training, specialized experts for visual aesthetics, bilingual text rendering, infographic generation, and image editing are optimized and distilled into a multi‑expert framework, yielding advances in image fidelity, complex composition, multi‑reference editing, and instruction following.
By Haiwen Diao, Jiahao Wang, Chenjing Ding, Hanming Deng, Jiangnan Chen, Ruixi Zhang, Ruohui Wang, Wenwen Tong, Xiangyu Fan, Yubo Wang, Yue Zhu, Yuwei Niu, Zhengqi Bai, Zhiqian Lin, Zhitao Yang, Zhongang Cai, Bo Yang, Chen Feng, Chengguang Lv, Guangjia Liu, Guanlin Wang, Hanyu Zhang, Haojia Yu, Hongcan Xiao, Hongli Wang, Huan Wu, Huaping Zhong, Jian Fang, Jianan Fan, Jiaqi Li, Jiefan Lu, Jing Zuo, Jingcheng Ni, Junxiang Xu, Linjun Dai, Mutian Xu, Peishen Yan, Penghao Wu, Ruijie Mao, Ruisi Wang, Shihao Bai, Shuang Yang, Shuya Yang, Shuyan Zheng, Silei Wu, Siying Li, Tao Chu, Tianbo Zhong, Tongxi Zhou, Weichao Luo, Weichen Fan, Wenhao Jia, Wenjie Gao, Xiangli Kong, Yan Li, Yang Yong, Zimo Wen, Zixuan Qian, Wenxiu Sun, Ruihao Gong, Quan Wang, Lewei Lu, Lei Yang, Ziwei Liu, Dahua Lin