Video AI fails quietly on conditions your cameras don’t see often. Spore finds those gaps, grows synthetic data to cover them, and turns that data into evals for training better models. Spore searched VAST’s video index for missing conditions: the highway cameras have 30 clips, mostly clear daylight, and none at night, in rain, in heavy fog or in snow. An LLM on W&B Inference, traced in Weave, ranks which gaps matter. Spore grows the missing weather onto real clips with a physics weather layer and NVIDIA Cosmos Transfer. The cars stay in place, so the labels carry over and testing runs automatically. On CoreWeave GPUs, it tests the models in the VSS pipeline: in light fog, YOLO finds only four in ten visible cars, while Cosmos Reason keeps counting them. The result is a new benchmark and training set for better models.
SpaceXAI (Cursor)
CoreWeave (Weights & Biases)
NVIDIA