Proposes a novel theoretical framework for understanding LLMs as resonance-holographic fields rather than static knowledge archives. Explores the phenomenon of 'subliminal learning' where models transfer behavioral traits through semantically unrelated data, suggesting that model weights create distributed interference patterns
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