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Blind cross-sensor spectral super-resolution can achieve unprecedented accuracy by learning the spectral transformation function directly from data, rather than relying on fixed assumptions.
LLM agents struggle to generalize from experience to reusable skills, often performing worse than simply replaying past trajectories, revealing a critical gap in current abstraction methods.
