Ant LingBot Releases World's First Embodied Native Pretrained Model LingBot-VA 2.0

On July 10, Ant LingBot, the embodied intelligence company under Ant Group, released LingBot-VA 2.0, claiming it to be the world's first 'embodied native' pretrained model. The model is designed from data, training objectives, to architecture specifically for robot physical world tasks, rather than adapting digital world models.

Core Capabilities and Performance

• Dual-arm task success rate: Achieves an average success rate of 93.6% on the RoboTwin 2.0 simulation benchmark, outperforming π0.5's 79.8% and the previous generation LingBot-VA's 92.2%.
• Inference speed: Single GPU inference latency reduced from baseline 965ms/chunk to 142ms/chunk, asynchronous control frequency increased from 33Hz to 225Hz.
• Real-world task tests:
  • Table tidying: Completes full tabletop organization, demonstrating long-term memory and state maintenance.
  • Conveyor belt grasping: Successfully grasps moving targets, achieving temporal alignment.
  • Chip grabbing: Stably picks up fragile chips without tactile feedback, showcasing fine manipulation.

Four Pillars of Technical Architecture

1. Causal pretraining: Trained from scratch using a causal architecture, the model can only predict the future based on the past, matching the temporal structure of robot closed-loop control, avoiding knowledge forgetting from bidirectional model adaptation.
2. Semantic visual-action tokenizer: While compressing visual information with VAE, additionally aligns semantic features from a frozen visual foundation model, and trains a latent action module to extract action supervision signals from unlabeled videos, enabling the model to understand 'how actions change the world'.
3. Sparse MoE architecture: The video backbone has approximately 13B total parameters, with only about 1.9B parameters activated per token during inference, balancing large capacity and low latency.
4. Foresight Reasoning asynchronous inference: While the robot executes the current action, the model predicts the next step in parallel, periodically calibrating with real observations to avoid prediction drift, achieving 'thinking while moving'.

Industry Ecosystem and Positioning

Ant LingBot also released LingBot-VLA 2.0, which has been adapted to 20 robot configurations from 17 manufacturers including Leju, Xingchen, and Zhiyuan, with deployments in scenarios such as logistics sorting and retail sorting. The company adopts a strategy of 'deploy one generation, pre-research the next', where VLA accumulates scenario data to feed back into VA iteration. LingBot-VA 2.0 is positioned as the pre-researched next-generation brain, complementing VLA.