RynnUnify: Towards Unified Embodied Brain

Minghao Zhu, Bohan Hou, Kun Xiang, Zixuan Wang, Yinzhou Tang, Yifei Yang, Zhikai Wang, Tianyi Zhang, Kehan Li, Haoyu Zhao, Siteng Huang, Leigang Qu, Sicong Leng, Dongchi Huang, Mingxiu Chen, Shijian Lu, Yue Wang, Xiaodan Liang, Zhenyu Zeng, Deli Zhao, Chengju Liu, Jianfei Yang, Xin Li · N/A · 2026

Summary

We introduce RynnUnify, a 16B unified embodied model that combines strong embodied understanding with long-horizon visual planning through interleaved video--language reasoning.

Abstract Summary

We introduce RynnUnify, a 16B unified embodied model that combines strong embodied understanding with long-horizon visual planning through interleaved video--language reasoning. With capabilities spanning spatial reasoning, physical prediction, and grounding, RynnUnify connects an understanding of the physical world with visual anticipation of how it evolves across successive subtasks. Building on these capabilities, we further develop an agentic extension through reinforcement learning to selectively use video predictions for reasoning about unobserved states and interaction outcomes. We also develop a world-action extension of the full RynnUnify model, translating its understanding and prediction capabilities into robot control. To evaluate these capabilities, we introduce Rynn-VP and Rynn-GEU for long-horizon visual planning and anticipatory reasoning. Experiments show that RynnUnify achieves the highest average embodied understanding score among the evaluated unified multimodal models and improves the Rynn-VP overall score by 9.75 points over Cosmos 3 Nano. Its agentic extension improves the Rynn-GEU average by 6.0 points over direct reasoning, while the world-action extension achieves 51.5\% success on 20 selected composite RoboCasa365 tasks and 46.7\% average success across three real-robot tasks. Together, these results establish RynnUnify as a shared foundation for embodied understanding, visual planning, and robot control, taking a step toward a unified embodied brain.

Key Points

We introduce RynnUnify, a 16B unified embodied model that combines strong embodied understanding with long-horizon visual planning through interleaved video--language reasoning.
With capabilities spanning spatial reasoning, physical prediction, and grounding, RynnUnify connects an understanding of the physical world with visual anticipation of how it evolves across successive subtasks.
Building on these capabilities, we further develop an agentic extension through reinforcement learning to selectively use video predictions for reasoning about unobserved states and interaction outcomes.
We also develop a world-action extension of the full RynnUnify model, translating its understanding and prediction capabilities into robot control.
To evaluate these capabilities, we introduce Rynn-VP and Rynn-GEU for long-horizon visual planning and anticipatory reasoning.

Model Architecture

As shown in Figure 2 , RynnUnify adopts the Mixture-of-Transformers (MoT) liang2025mixtureoftransformers architecture of Cosmos3 cosmos3 . The model comprises an autoregressive reasoner for multimodal understanding and language generation, and a diffusion generator for predicting continuous visual latents through rectified flow liu2022flow .

The two branches use separate Transformer parameters and interact through joint attention, allowing the generator to attend to representations produced by the reasoner.

Model Architecture — RynnUnify: Towards Unified Embodied Brain — Figure 1 : RynnUnify unifies embodied understanding, visual planning, agentic reasoning, and robot action.

Data

Table 3.1 summarizes the data used to fine-tune the reasoner and generator. To maintain broad embodied understanding and reasoning capabilities, we use a 10% subset of the RynnBrain pretraining corpus li2026rynnbrain11capablegeneralizable as the foundation of our understanding data.

Reasoning about embodied tasks requires the model to interpret task intentions, recognize objects and their properties, understand spatial relationships, and track changes across successive observations.

Data — RynnUnify: Towards Unified Embodied Brain — Figure 2 : Overview of the RynnUnify architecture. (a) An autoregressive reasoner and a rectified-flow video generator interact through joint attention. (b) Memory-conditioned video generation supports I2V, FL2V, and autoregressive video continuation. Scene-memory frames provide persistent visual references, while a fixed temporal prefix carries recent motion and interaction context across consecutive clips.

Sources and Demonstrations

Figures are reproduced from Zhu et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.