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Robots can learn faster and perform better by strategically choosing *when* to be unpredictable, achieving both multimodal expressivity and deterministic efficiency.
Ditch brittle token rankings: OccamToken uses register-anchored relative evidence testing to prune visual tokens in VLMs, achieving extreme compression (down to 1.4%!) without retraining or significant accuracy loss.
Robots can now better understand implicit instructions in cluttered scenes, choosing the knife over the scissors when told to "cut the apple," thanks to a new benchmark and neural network architecture designed to handle functionally similar objects.
