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The structure of the Voynich text and how it may be generated

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  • The structure of the Voynich text and how it may be generated
• The structure of the Voynich text and how it may be generated

• The structure of the Voynich text and how it may be generated

  quimqu > 3 hours ago

  Most discussions about the Voynich already agree on a few basic points: the text is not random, similar words tend to appear near each other, and position within the line matters. What is less clear is how this structure is actually generated.
  
  I have been working in a pipeline that lets me analyze the structure of the MS. The goal of this analysis was not to describe patterns, but to test concrete generative hypotheses against the data. The question was always the same: if this were the real mechanism, would it reproduce what we observe? Running different models through the same pipeline makes it possible to discard entire classes of explanations, not just speculate about them.
  
  

  Hypothesis	Expected behavior	Observed failure
  Random / weak structure	No stable local similarity or positional effects	Strong clustering and positional patterns persist
  Sequential (Markov-like)	Next token predictable from previous ones	Bigram/HMM models add little or collapse
  Copy–modify (parent-based)	Clear local derivations, strong nearest neighbor	Generative models produce too much similarity
  Single dominant parent	One best local candidate per token	Multiple candidates with similar scores, no clear winner

  
  The important point is not just that these models fail, but how they fail. Copy-and-modify mechanisms generate too much similarity, producing tight chains of derived forms that are not observed in the real text. Sequential models fail in the opposite direction, missing most of the structure entirely. The idea of a single dominant parent breaks down because the local neighborhood is too ambiguous: for most tokens, several nearby forms are equally plausible, with no clear winner. These are structural mismatches, not minor errors, and they rule out a large class of simple generative explanations.
  
  At the same time, some effects are very robust. Local similarity is real and strong: words share substrings and cluster in form space. Position within the line has a clear impact on length, prefixes, and suffixes. But these signals do not translate into a simple mechanism where one word determines the next. Token-level models struggle precisely because the system is not organized primarily as a chain of local decisions.
  
  The structure becomes clearer when moving to the level of the full line. If lines are represented as whole objects, using their internal properties (number of tokens, length distributions, entropy, positional patterns), they fall into a small number of latent types. These types are not imposed manually, but learned directly from the text. They correspond broadly to different functional roles, but also reveal variation within them. These data-driven line types also show persistence across consecutive lines, suggesting that the manuscript is organized as sequences of line-level states, not just as a stream of loosely connected tokens.
  

  • The text is not governed by simple sequential rules. Token-to-token models fail to capture the structure, even when extended beyond basic Markov assumptions.
    
  • It is not generated by copy-and–modify or parent-based derivation. These mechanisms overproduce similarity and impose chains that are not present in the data.
    
  • There is no single dominant local source for most tokens. The local neighborhood is too ambiguous, with multiple equally plausible candidates.
    
  • The strongest and most stable structure appears at the level of the full line. Lines form a small number of latent types with distinct formal profiles and non-random sequencing.
    

  
  A useful way to think about it is the following: 
  
  

  1. The line type defines a space of possible forms. 
     
  2. The local context restricts this space further by favoring forms that are compatible with nearby words. 
     
  3. But within that constrained space, the final choice is weakly determined. Many candidates are acceptable, and no single one is strongly preferred. 
     

  
  This explains why local similarity is strong but does not translate into clear parent-child relationships, and why token-level models struggle while line-level structure is much more stable.
  
  With this analysis I try not to show that the Voynich is structured, which was already suspected, but to narrow down the class of mechanisms that can plausibly generate it. Simple sequential models and naive copy-and-modify processes do not fit. Models that operate at the level of line-level states, combined with a local compatibility field and weak selection within that field, are much more consistent with the data.

• RE: The structure of the Voynich text and how it may be generated

  quimqu > 2 hours ago

  I know that my posts are quite dense and "hard" to read. So I try to summarize here:
  
  What remains consistent with the data is not a chain of transformations, but a constrained selection process. The line defines a space of valid forms, the local context restricts this space further, and the final choice is made among multiple equally compatible candidates. This shifts the problem from “how is the next token generated” to “how is the space of valid tokens defined”.
  
  The one can think of concrete and testable (I will work on them) assumptions about the mechanism. Instead of trying to predict a single next token, the model should first approximate a candidate set, and only then model selection within that set.
  
  

  Assumption	Operational test
  Candidate set constrained by line position	Predict top-k tokens from line features and measure recall
  Local context restricts form, not identity	Build candidates by similarity and check if real token is included
  Selection within the set is weak	Train ranking model and measure score flatness
  Substructures drive compatibility	Use prefixes/suffixes only and test predictive power

  
  With this, a generative model would not sample tokens directly from a sequence model, but proceed in two steps: first generate or retrieve a set of compatible forms given the line and its context, then sample from this set with relatively low discrimination. The key quantity to model is no longer the probability of a token given the previous one, but the probability that a token belongs to the current feasible set.
  
  This leads to a different type of validation. A good model is not one that predicts the exact next token, but one that consistently places the real token inside a small, well-defined candidate set.
  
  I hope to have explained myself better 

• RE: The Structure of the Voynich Text and How It May Be Generated

  nablator > 2 hours ago

  (3 hours ago)quimqu Wrote: You are not allowed to view links. Register or Login to view.Simple sequential models and naive copy-and-modify processes do not fit.

  
  I tried to propose a less naïve self-citation generation method including sparse initialization causing bottlenecks that would explain local inhomogeneities, lazy source words selection patterns, non-sequential writing, and generation rules optimized to fit the transliteration data. It seems that no one knew how to test the hypothesis. What do you think about it? You are not allowed to view links. Register or Login to view.
  
  Such a method, if it can be shown to replicate all known properties of Voynichese, does not exclude the possibility of a cipher such as You are not allowed to view links. Register or Login to view., but unnecessary complications can usually be discarded by Occam's razor when there is no evidence specifically pointing at them.

• RE: The Structure of the Voynich Text and How It May Be Generated

  quimqu > 2 hours ago

  (2 hours ago)nablator Wrote: You are not allowed to view links. Register or Login to view.I tried to propose a less naïve self-citation generation method including sparse initialization causing bottlenecks that would explain local inhomogeneities, lazy source words selection patterns, non-sequential writing, and generation rules optimized to fit the transliteration data. It seems that no one knew how to test the hypothesis. What do you think about it? You are not allowed to view links. Register or Login to view.
  
  Such a method, if it can be shown to replicate all known properties of Voynichese, does not exclude the possibility of a cipher such as You are not allowed to view links. Register or Login to view., but unnecessary complications can usually be discarded by Occam's razor when there is no evidence specifically pointing at them.

  
  I think your proposal is interesting, especially because it goes beyond naïve copy–modify and introduces more realistic selection patterns.
  
  The key issue, in my view, is whether it can reproduce two constraints at the same time: strong local similarity and weak determinism. In the data, many nearby forms are compatible, but there is usually no clear source or dominant parent. Most direct self-citation models tend to produce too much chaining and identifiable source–target links.
  
  I would suggest testing two versions of your idea: a strong one where tokens are explicitly derived from source words, and a weaker one where nearby words define a candidate set, but the final choice is not tied to a specific source. The question is whether the strong version can avoid producing detectable parent-child structure.
  
  If you agree, I will try to model them and put them in my pipeline. Will let you know!
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