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Today's frontier agents virtually collapse in professional engineering environments, solving just 3.6% of multi-software workflows when held to real-world physical and geometric constraints.
Robots fail out-of-distribution because monolithic VLA policies hide task state and recovery logic; treating physical execution as inspectable, debuggable code enables closed-loop failure recovery and continuous self-evolution.
Current AI systems struggle to conduct independent scientific research, with performance plummeting by nearly 50% when human guidance is removed.
Multimodal graph unlearning doesn't have to destroy utility: carefully protecting high-dimensional input projections during the unlearning process preserves performance while still enabling effective forgetting.
