# RynnBrain 1.1: Towards More Capable and Generalizable Embodied Foundation Model

arXiv: https://arxiv.org/abs/2607.17977 · HTML: https://mlsanity.com/paper/2607.17977/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2026.

## Built on by (most cited first, 8 of 8)

- [PhyAI: Real-Time Physical AI at the Edge, Scalable Rollouts in the Cloud](https://mlsanity.com/paper/2608.03682/) (2026)
- [DELE-w0.5: Inferring Action from Future Latent State for Robotic Manipulation](https://mlsanity.com/paper/2608.22067/) (2026)
- [ME-VLM: A Unified VLM for Embodied Cognition and Agent Coordination](https://mlsanity.com/paper/2609.24526/) (2026)
- [PhysBrain 1.5: From Vision-Language Models to Physical Foundation Models](https://mlsanity.com/paper/2609.14973/) (2026)
- [CogWAM: Aligning Semantic Cognition with World Action Modeling via Event-Driven Interfaces](https://mlsanity.com/paper/2609.37721/) (2026)
- [GroundAnything: Reconciling Parallel Decoding with Precise Visual Grounding at Flash Speed](https://mlsanity.com/paper/2609.39600/) (2026)
- [GroundingPI: A Grounding Foundation Model towards Physical Intelligence with Visual Primitives](https://mlsanity.com/paper/2609.39601/) (2026)
- [ABot-ER: Unified Embodied Reasoning through Agentic Data-Model Co-Evolution](https://mlsanity.com/paper/2610.18355/) (2026)

## Abstract (arXiv)

We present RynnBrain 1.1, a family of embodied foundation models spanning 2B, 9B, and 122B-A10B scales. Trained with a unified spatio-temporal and physically grounded framework, RynnBrain 1.1 supports embodied perception, spatial reasoning, localization, and planning. Compared with RynnBrain 1.0, it further introduces contact-point prediction across the model family and native 3D grounding for the 2B and 9B models, yielding representations and outputs that are more directly aligned with robot manipulation. We also develop RynnBrain-VLA with a unified cross-embodiment action space and embodiment-specific masking, and deploy it on Unitree G1, Astribot-S1, and Tianji-Wuji. RynnBrain 1.1 achieves strong results on embodied cognition, localization, and 3D grounding, with the 122B-A10B model outperforming all evaluated proprietary and open-source models on VSI-Bench, MMSI, and RefSpatial-Bench. Real-robot experiments show that RynnBrain-initialized policies outperform Qwen-based and representative generalist VLAs, while joint multi-task and multi-embodiment training improves process scores and success rates over per-task training.