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April 30, 20260 citationsOpen Access

Evidence for a Structured Token-Generation System in the Voynich Manuscript

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YCYoungsan Chang

Key Points

  • The aim is to test if the Voynich Manuscript can be modeled with a structured token-generation system instead of random processes.
  • Analyzed Voynich Manuscript tokens using the Zandbergen–Landini EVA transcription (ZL3b).
  • Evaluated metrics including matching rate, token coverage, and Zipf distribution alignment.
  • Conducted statistical tests for significance and examined morphological family structures.
  • PCS token matching rate was 97.02% compared to 5.07% for random generation.
  • Token coverage was 85.25% for PCS versus 18.50% for random models.
  • Significant differences in Zipf slope, transition entropy, and compression ratios were observed.

Abstract

This record provides the preprint and reproduction package for the study "Evidence for a Structured Token-Generation System in the Voynich Manuscript." The study tests whether Voynich Manuscript tokens can be modeled by a structured prefix–core–suffix (PCS) token-generation system rather than by random processes. Using the Zandbergen–Landini EVA transcription (ZL3b), the analysis evaluates token matching, coverage, Zipf distribution alignment, fixed-length validation, bootstrap significance, holdout generalization, inter-token transition entropy, morphological family structure, positional dependency, and compression-based structural regularity. Key results include: - Matching Rate: PCS 97.02% vs Random 5.07%- Token Coverage: PCS 85.25% vs Random 18.50%- Zipf Slope Difference: PCS 0.0795 vs Random 0.7138- H(suffix | core): 0.8746- Transition entropy reduction: 0.2504 bits- Compression ratios: real 0.3089, shuffled 0.3240, PCS-generated 0.3163- Morphological families: 767 core-sharing families and 1,017 suffix-alternating clusters- Positional chi-square: prefix = 4554.31, core = 17993.46, suffix = 3329.07 The results support a structured token-generation mechanism but do not constitute semantic decipherment.

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Cite This Study

Youngsan Chang (2026) studied this question.

synapsesocial.com/papers/69f2f1dc1e5f7920c63878efhttps://doi.org/10.5281/zenodo.19858095
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