What Lies Beneath: Statistical Structure in Voynichese Revealed by Transformers

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This feels to me like one of the most pervasive misconceptions in modern Voynich research, up to the academic level. EVA makes just as many choices as Currier did, and one is not necessarily closer to the truth than the other.

I surely agree with you! Would then be okay to say EVA is more 'analytic' in its choices while Currier is more 'synthetic'?

Thanks for the comments.

To add a practical perspective: I’ve now trained the same GPT model using the Zandbergen EVA transliteration, and the results are remarkably close to what I obtained with Currier:

- Average validation loss: 1.2651

- Perplexity: 3.54

So from a modeling perspective, both systems encode a similarly learnable structure — neither seems clearly “closer to the truth,” at least in terms of predictive consistency.


That said, something interesting appeared when comparing the two heatmaps of average word loss per folio: both show a fairly similar overall pattern. However, the Z-L EVA model includes data for folio You are not allowed to view links. Register or Login to view. — the famous “wheel with text in the spokes” page often described as astronomical, cosmological, or magical — while the Currier model is unable to analyze this folio. Despite this difference, both models produce folio-by-folio perplexity values that are quite similar in absolute terms. Some folios show higher perplexities than average, others lower, but the distribution between the two heatmaps is comparable.


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So even though EVA and Currier make different choices (and yes — I like the idea that EVA is more analytic while Currier is more synthetic), the internal structure captured by the model remains stable across systems.

The differences may lie more in how each system segments glyphs and sequences — which could affect how we interpret specific outliers like f57r.

More to explore here!

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Yes, you got it mostly right! GPT models learn patterns and rules (like a kind of “grammar”) from the Voynich text. They use this knowledge to predict or generate new words. Then, by comparing these generated words to the actual Voynich words, the model calculates a “perplexity” score, which shows how well it understands the text. Lower perplexity means better understanding. This score is also used during training to improve the model step by step.