LeCun's AMI open-sources H-JEPA, a hierarchical world model for visual planning
AMI (Advanced Machine Intelligence), the world-model startup chaired by Yann LeCun, released a paper with NYU, INRIA Paris and Brown University describing H-JEPA: multiple JEPA modules stacked so each layer predicts states at its own timescale, with a high level setting a coarse plan and lower levels refining predicted intermediate states into executable actions. The paper reports 73% success for a three-layer model versus 18% for a single-layer model on the Visual AntMaze simulated maze task, with better results at lower planning compute on several tasks; code and pretrained weights were released. The report also says AMI previously raised $1.03B at a $3.5B pre-money valuation, while its website still shows mainly research direction and hiring rather than a public product.
It is the first public technical deliverable from LeCun's post-Meta startup, putting a hierarchical, open-weight JEPA route to world models alongside LLM-centric approaches.