Ant Group's Robbyant Unveils LingBot-VA 2.0, a Causal Video-Action Model for Physical AI

Robbyant, the embodied AI unit inside Ant Group, has released the LingBot-VA 2.0, the first embodied-native foundation model. It describes a video-action foundation model for generalist robot manipulation. The research team pretrains the whole stack for embodiment instead of fine-tuning a video generator.

Most video-action models reuse two components built for digital content creation. One is a reconstruction-oriented VAE. The other is a bidirectional video-diffusion backbone, with an action module attached.

This creates three limitations. Pixel-reconstruction latents preserve appearance but carry limited physical structure. Iterative denoising over video tokens is too slow for closed-loop control.

Generic video objectives never teach how actions reshape the world. A fourth mismatch is structural. Backbones use bidirectional attention, while control unfolds strictly forward in time.

LingBot VA Version 1.0 finetuned that stack into a causal model. Version 2.0 pretrains a causal DiT natively. Building on that motivation, stage one replaces the compression-only VAE.

Following RepWAM, the tokenizer adds two objectives to reconstruction. Semantic alignment pulls visual latents toward a frozen Perception Encoder teacher. A latent-action objective extracts compact transition variables between consecutive latents.

An inverse dynamics model predicts each latent action. A forward dynamics model decodes it into a transport map plus residual. World states and actions now share one latent space.

Unlabeled web video therefore carries action-relevant supervision. On top of that space, version 2 pretrains a causal DiT. It keeps the Mixture-of-Transformers layout of version 1.0.

A video expert and an action expert share one causal self-attention. Each owns a separate feed-forward pathway. The two streams scale asymmetrically.

The video expert replaces its dense FFN with a sparse MoE routed layer. That layer holds 128 routed SwiGLU experts, top-8 routing, one shared expert. Load balancing follows the auxiliary-loss-free Loss-Free Balancing strategy.

The action expert keeps a dense FFN at hidden dimension 768. The video backbone is roughly 13.0B parameters, about 1.9B active. With the action expert and MCP heads, training covers about 15.3B parameters.

Roughly 2.5B activate per token at inference. Training uses a rectified-flow objective with a hybrid Muon plus AdamW optimizer. Beyond architecture, two objectives shape what the model learns.