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MOJO outperforms traditional supervised learning models by effectively utilizing unlabelled data, achieving state-of-the-art results in neural decoding tasks with minimal labelled input.
Control interventions are often detected by LLMs, with awareness levels varying significantly across models and tasks, revealing vulnerabilities in AI safety protocols.
Offline policy optimization with a world model allows for affective music recommendation that improves user valence and arousal, even when ethical constraints preclude online experimentation.
