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Physical robot learning can now autonomously discover and exploit informative experiments to continuously improve foundation models, achieving 77% success on RoboCasa365 and zero-shot transfer to real hardware.
ZEBRA is a fully automated verification and bug-detection framework that reduces zkVM verification to a solution-set cardinality problem over a canonical trace space, where redundancies such as null-row padding and non-deterministic permutations are eliminated prior to counting.