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Distilled models in high dimensions don't just learn; they copy their teacher's noise-data pairings, revealing unexpected constraints in their geometric freedom.
Looped LLMs don't just perform better reasoning, they also internally mirror the distinct inference stages of standard feedforward models, repeating them cyclically.
