Oddin：通过图像生成自蒸馏实现统一多模态模型中的潜在视觉推理
Oddin: Latent Visual Reasoning in Unified Multimodal Models via Self-Distillation from Image Generation
AI 导读
论文提出 Oddin 框架，让统一大规模多模态模型（ULMMs）以紧凑潜在 token 替代中间图像完成潜在视觉推理，潜在 token 全部由理解分支处理，生成分支只负责输出最终图像。
正文 · 原文
Abstract:Unified large multimodal models (ULMMs) are able to perform visual reasoning through interleaved text-image generation, but generating explicit intermediate images incurs significant computational overhead. While visual latent reasoning addresses this, most existing methods rely on vision-language models (VLMs) incapable of visual generation. We propose Oddin, a novel framework enabling latent visual reasoning in ULMMs. Oddin replaces intermediate images with compact latent tokens processed entirely by the understanding branch, reserving the generation branch for the final output image. We train the model via offline self-distillation with three objectives, final image generation losses, text distillation losses, and cross-entropy losses for special tokens. Oddin surpasses interleaved generation baselines and closed-source VLMs on average across seven VBVR-Pro visual reasoning tasks. Qualitative analysis reveals that latent tokens attend to relevant regions in an input image, demonstrating that ULMMs can perform complex visual reasoning without generating intermediate images.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.33062 [cs.CV] |
| (or arXiv:2610.33062v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33062 arXiv-issued DOI via DataCite |
Submission history
From: Hankyeol Kim [view email]
[v1]
Mon, 19 Oct 2026 14:07:28 UTC (1,010 KB)
来源：arXiv cs.AI · arxiv.org