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This paper presents a scaling law showing that the contextual influence of word order in human language decays approximately as 1/d with distance, as measured by the reduction in perplexity from large language models, across multiple languages and corpora.
This paper introduces a semantic-timescale analysis pipeline to study how generic vs. specific content is distributed over time in human and AI-generated speech, revealing that autocorrelation-window measures capture temporal organization of semantics beyond static lexical distributions.