We're releasing HY-Embodied-0.5, a family of foundation models for real-world…
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We're releasing HY-Embodied-0.5, a family of foundation models for real-world embodied agents. The 2B model is now open source. It strengthens spatial-temporal perception and embodied reasoning for prediction, interaction, and planning. 🤖 The suite includes: 🔹 2B for edge deployment 🔹 32B for complex reasoning Key innovations: 🔹 Mixture-of-Transformers (MoT) architecture for modality-specific computation 🔹 Latent tokens for improved perceptual representation 🔹 Self-evolving post-training 🔹 On-policy distillation from large to small models Across 22 benchmarks, the 2B model outperforms similarly sized SOTA systems on 16 tasks. The 32B model approaches frontier-level performance. 🔗 GitHub: https://t.co/LknJzqiNOX 🤗 Hugging Face:
Context
Tencent's HY-Embodied-0.5 is designed to help physical agents interpret scenes and reason about actions. Its release documentation describes transferring reasoning and planning behavior from a larger model into a compact variant intended for deployment near the device. The released MoT-2B model has roughly four billion total parameters and activates about 2.2 billion during . Its small-model designation therefore describes active computation, while the intended role is perception and reasoning within a broader robot-control system.
- inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
- AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
- token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
- distillation — Training a small, cheap model to imitate a big one's outputs, keeping much of the capability at a fraction of the cost.
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