Quote:Technical rebuttal: semantic density and the “Naibbe” verbosity constraint." Cryptologia, ahead-of-print(ahead-of-print), pp. 1–2

Quote:I’m pleased to share that my paper, “Technical Rebuttal: Semantic Density and the ‘Naibbe’ Verbosity Constraint,” has been accepted for publication in Cryptologia and is now in production with Taylor & Francis.

The paper examines a focused technical question in Voynich Manuscript research: whether short, compact Voynich-like ciphertext units can, in principle, preserve greater local information density than the Naibbe cipher model appears to permit.

Using a deliberately limited toy model, I illustrate how a cipher architecture incorporating positional behavior, nulls, permutation, and abbreviation could encode more plaintext information while retaining compact ciphertext forms.

The paper does not claim a decipherment of the Voynich Manuscript. Its purpose is narrower: to clarify what conclusions can—and cannot—be drawn from the Naibbe model regarding the possible information density of alternative Voynich-like cipher systems.

IDH Wrote:Der Fokus soll dabei auf einer allgemein verständlichen Darstellung liegen, die evtl. auch verschiedene Medien zur Vermittlung der Inhalte nutzt. Ziel ist es, die Inhalte auf der Plattform You are not allowed to view links. Register or Login to view. der Öffentlichkeit zur Verfügung zu stellen.

The focus should be on presenting the information in a way that is accessible to everyone, possibly using various media to convey the content. The goal is to make the content available to the public on the platform You are not allowed to view links. Register or Login to view. .

Quote:In two recent companion papers, Layfield and Davis apply latent semantic analysis (LSA) to the Voynich Manuscript and derive two hypotheses: that the manuscript was conceived as a sequence of single-bifolium quires (“singulions”) later bound out of order, and that the intended sequence of bifolia can be recovered by maximizing textual similarity across page transitions. This review examines both hypotheses against the papers’ own published data. The empirical core of the first hypothesis, an elevated textual similarity within physical bifolia, is real and independently confirmed. The reading-order interpretation built upon it, however, is underdetermined by the data and contradicted by the authors’ own explanation of the herbal section. The second hypothesis rests on the premise that the intended order of a meaningful text maximizes lexical similarity between adjacent pages. This premise is refuted by the authors’ own calibration manuscripts, and the resequencing experiment lacks two controls that would make its reported improvements interpretable: a permutation null distribution and a calibration on deliberately shuffled known text. A similarity-based resequencing of the manuscript is nevertheless methodologically sound under one specific model of the text, namely that page-to-page similarity reflects proximity of production rather than continuity of meaning. Three concrete experiments, each executable with the authors’ existing pipeline, would discriminate between the competing interpretations.

Quote:Layfield and Davis apply Latent Semantic Analysis to the Voynich Manuscript and engage with a substantial prior literature on the manuscript's two Currier languages, A and B. This note documents four places where the paper's characterization of cited sources does not match what those sources say. In one case, the cited classifications differ from the citing paper's representation of them, and the cited author's disclaimers about the labels are not carried into the citing paper. In another, the cited paper's terminology is attributed from a different paper, and the cited paper's own contribution on the same question is not represented. In a third, the cited paper does not address the topic of the citation. In the fourth, the cited paper draws the opposite conclusion from the one it is cited to support. Each can be verified directly by readers consulting the cited works.

• First, a generative (unsupervised) model looks at the raw character counts with no knowledge of Currier's labels and asks: how many distinct groups does the data itself support? It independently selects two groups and assigns pages in a way that substantially overlaps with Currier's A/B split.
  

• Second, and perhaps more importantly, a predictive model tests whether knowing a page's A/B label actually lets you forecast its character statistics on unseen pages. The result: it predicts held-out page labels at 89.2% accuracy in character-pair ratios on text the model has never seen.