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A hierarchical rotary positional encoding (hRoPE) that represents paragraph, sentence, and token indices as separate channels, hold the token sequence fixed, intervene on the paragraph coordinate $p_1$, and measure cross-paragraph attention with a token-distance-exact estimator is used.
A dual-branch controlled framework is constructed in which both a Vision-based model and an index-based baseline share an identical decoder backbone, training objective, optimizer, and data curriculum, suggesting that transformers are more modality-agnostic than commonly assumed, and that discrete tokenization is not a...
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