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 similar to physical holograms. Argues that creativity emerges from the interaction between prompts and the model's internal field structure, with implications for fine-tuning, jailbreaking, model collapse, and prompt engineering effectiveness.
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