ReLearn at CVPR 2026: can AI still learn from humans?

ReLearn, a CVPR 2026 workshop co-organized and sponsored by Lambda, examined what artificial intelligence can still learn from human intelligence as foundation models grow through scale. Speakers from computer vision, cognitive science, and embodied AI explored the benefits and limits of human-inspired learning, emphasizing that even ostensibly self-supervised systems often encode human assumptions through representations, augmentations, and objectives. Discussions focused on the difficulty of developing persistent spatial and physical world models from perception, with research on developmental spatial reasoning, continuous visual experience, egocentric first-person data, explicit 3D reasoning, and models of physical objects. The workshop suggested that AI may need structured representations shaped by memory, action, interaction, and partial observation to operate effectively in the physical world, while cautioning that systems should not simply replicate human cognition. Its central conclusion was that future progress may depend not only on scaling data and computation, but also on determining which human-derived principles provide useful inductive biases and which forms of structure machines should discover independently.

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