Orca combines video, speech and visual questions in a unified latent space, demonstrating that this unified world model can outperform specialized models in text, image and action forecasting tasks.
Orca learns a shared world representation from videos and language annotations, enabling text generation, image prediction, and agent control with a frozen backbone and modular decoders.
Qwen-AgentWorld trains language models on over 10 million interaction trajectories as an environment simulator to train AI agents through virtual environments and improve their performance across seven benchmarks.
Qwen-AgentWorld leverages language models as learned environment simulations to efficiently train autonomous agents and improve their reasoning through chain-of-thought prompting.
Visual world models can be systematically manipulated through visually imperceptible image modifications to generate erroneous predictions without requiring knowledge of future data or user inputs.
