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README.md

@@ -48,7 +48,7 @@ To deliver strong physical-world understanding and interaction capabilities whil
 
 ## ⭐️ Key Features
 
-  * 🧠 **Efficient 30B MoE, ~3B activated** — Combines the Hy3-A3B language backbone with the Hy-ViT2 vision encoder in a Mixture-of-Experts architecture. Total ~30B parameters with only ~3B activated per token — approximately one-tenth of the activated parameters of the previous-generation A32B system, while achieving nearly comparable overall performance.
+  * 🧠 **Efficient MoE, ~3B activated** — Combines the Hy3-A3B language backbone with the Hy-ViT2 vision encoder in a Mixture-of-Experts architecture. Only ~3B parameters are activated per token — approximately one-tenth of the activated parameters of the previous-generation A32B system, while achieving nearly comparable overall performance.
   * 🌏 **Action-Centric Capability Taxonomy** — We define three progressive levels of embodied intelligence: (i) **Action-Relevant State Understanding** for accurately understanding the states of the agent and its environment, (ii) **Action–Transition Reasoning** for understanding actions, planning them, and reasoning about their consequences, and (iii) **Sequential and Adaptive Reasoning** for long-horizon planning, reflection, repair, and recovery. Data and training are systematically designed around this taxonomy.
   * 🔁 **Self-Evolving Post-Training** — Embodied agentic reasoning is cultivated through a self-evolving loop that couples reinforcement learning with rejection-sampling fine-tuning, seeded from a small curated set of high-quality thinking traces. A final reward-specialized stage trains continuous-reward and discrete-reward RL policies separately and fuses them, delivering sharp geometric precision alongside robust decision-making, planning, and reflection quality.
   * 🏆 **State-of-the-Art on Embodied Benchmarks** — Ranks 1st on 19 of 38 benchmarks and 2nd on another 11, outperforming Qwen3.6-A3B (+4.4% avg), Cosmos 3-8B, and Embodied-R1.5-8B. State-of-the-art on R2R-CE vision-and-language navigation (RGB-only setting) and strong zero-shot performance on Matterport3D Object Goal Navigation.