Tencent Hy‑Embodied‑RxBrain‑1.0: 6.2B Multimodal Foundation Model for Embodied Cognition
Integrate RxBrain 6.2B multimodal foundation model for embodied cognition into your applications to enable joint subgoal planning and world‑state prediction.
Integrate RxBrain 6.2B multimodal foundation model for embodied cognition into your applications to enable joint subgoal planning and world‑state prediction.
Summary
Tencent’s Hy‑Embodied‑RxBrain‑1.0, a 6.2‑B‑parameter multimodal foundation model, unifies language reasoning and visual imagination to deliver embodied understanding, world‑state prediction, and joint subgoal planning.
The architecture employs a Unified Mixture‑of‑Transformers backbone with modality‑specific pathways, allowing text, vision, and generation to share a single autoregressive model. A flow‑matching image head decodes imagined frames into a frozen FLUX VAE latent space, enabling multi‑frame world‑model rollouts and goal‑image planning.
Interleaved reasoning and imagination generate a sequence that alternates between textual reasoning and imagined frames, coupling symbolic plans with visual goals. The model supports chain‑of‑thought over images and multi‑frame video, and emits both next action and goal image for each subgoal. The release demonstrates the potential for embodied AI applications that require both textual and visual reasoning.
Key changes
- 6.2B‑parameter backbone with modality‑specific pathways (text/vision/generation)
- Unified Mixture‑of‑Transformers (MoT) architecture
- Flow‑matching image head decodes into FLUX VAE latent space
- Interleaved reasoning + imagination sequence alternates text and frames
- Emits next action and goal image for each subgoal
- Supports chain‑of‑thought over images and multi‑frame video