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| Format: | Recurso digital |
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Zenodo
2026
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| Online-Zugang: | https://doi.org/10.5281/zenodo.18918719 |
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Inhaltsangabe:
- Proves that universal entanglement in transformer representations is a geometric consequence of high-dimensional encoding via the Johnson-Lindenstrauss lemma and concentration of measure. The Entanglement Theorem shows that for any d-dimensional representation with k concept-informative directions, random m-dimensional projections with m > 32k preserve entanglement with probability exceeding 1 - 2exp(-m/128). Experimental validation across GPT-2 to Qwen-7B confirms the theoretical predictions.