What Lies Beneath: Statistical Structure in Voynichese Revealed by Transformers

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The high percentage of conformity suggests that the generation process is strongly guided by structural constraints similar to those observed in actual Voynichese. While not all words match real entries from the manuscript, most invented forms remain within plausible morpho-phonological boundaries defined by the slot grammar. This supports the idea that the model is not producing random noise, but instead approximates a coherent internal system—whether artificial or natural.

Could you in theory test for non-conforming words already in the VM? Or i guess measure how much they break the internal rules? I wonder if it could be interesting to see where these pop up in the VM, if they are predominant in "labelese" or maybe in certain sections? Or certain scribes? To my knowledge this hasnt been done yet but I am probably wrong.

Hi!

Thanks for the thoughtful suggestion — I think you're absolutely right that identifying non-conforming words and tracking their locations in the manuscript could reveal meaningful patterns.

What I’ve done so far is train a character-level GPT model on Voynichese text (using Currier transliteration). Then, I used this model to estimate the average word-level loss for each token in the manuscript — essentially measuring how well the model “understands” each word given its context.

I attach here a png fie of the interesting resulting heatmap of loss, so you can easily see which folios are the most strange ones according to GPT. Surprisingly, the last folios have the lowest loss.



From this, I have been able to:

• Compute the average loss per folio, showing how predictable the text is in different sections.
  
• Visualize this data as a heatmap, coloring folios by their average word loss.
  

This framework opens up possibilities exactly along the lines you suggested:

• Highlighting words or regions where the model struggles most.
  
• Investigating whether these “high-loss” zones correlate with labelese, specific sections, or particular scribes (if metadata is available).
  
• Zooming in on the individual words the model finds most anomalous, and seeing their frequency and distribution.
  

I haven’t done the full detailed analysis yet, but the infrastructure is ready and the heatmap helps guide further exploration.

Below is a BBCode table showing the top 30 most anomalous words in Currier transcription ranked by loss (i.e. the words the model found hardest to predict):


? Top 30 most anomalous words (by loss):

Word	Loss	Freq	Len
RH	10.1558	1	2
J	9.8573	3	1
E	9.8573	15	1
6	9.8573	15	1
B	9.8573	2	1
Q	9.8573	2	1
F	9.8573	4	1
9	9.8573	53	1
3	9.8573	1	1
R	9.8573	35	1
4	9.8573	4	1
8	9.8573	32	1
Z	9.8573	9	1
A	9.8573	1	1
2	9.8573	121	1
C	9.8573	1	1
D	9.8573	3	1
O	9.8573	18	1
9J	9.4344	1	2
FT	8.8774	1	2
FU	8.5493	1	2
OT	8.3373	1	2
8DE	8.2584	1	3
OU	8.0167	1	2
O3	7.5268	3	2
9EJE	7.5207	1	4
P3	7.4110	1	2
8R	7.3208	1	2
ON	7.2464	2	2
QE	7.0748	1	2

These words often consist of very short tokens, sometimes single characters or rare combinations, which the model struggles to predict confidently. Investigating where these words cluster — whether in labels, particular manuscript sections, or scribes — could provide insights into the structure or anomalies within the text.

To my knowledge, a comprehensive analysis of “non-conforming” words in the Voynich Manuscript has not yet been performed at this level of detail, so this approach offers a promising direction for further research.

If you or anyone else is interested, I’d be happy to collaborate or share the tools I’ve developed so far.