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Seoul National University
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Learned representations on a hypersphere unlock closed-form planning and adaptive waypoint generation, enabling complex manipulation from scratch where baselines fail.
Hamiltonian property testing and learning with only forward evolution cannot achieve Heisenberg-limited scaling, requiring $\Omega(1/\varepsilon^2)$ query time.