26-02-2026, 08:25 PM
We observed a consistent pattern when comparing the Voynich text with natural languages and with Torsten Timm’s generated text. If we compute character-level mutual information MI(d) and then randomly shuffle whole words (tokens), the long-range MI of natural languages changes very little. The overall shape of the tail remains almost the same.
In contrast, when the same word shuffle is applied to the Voynich text or to Torsten Timm’s generated text, the long-range MI decreases clearly. (I have carried out bootstrap tests and the difference is statistically significant). This behavior has also been noted by Nablator, who reported a similar stability in natural languages and a reduction in Voynich after word shuffle.
To understand where the effect comes from, I repeated the analysis inside the Voynich manuscript:
The results show that the effect is not uniform across the manuscript. The Herbal section accounts for a large share of the global long-range gap. Some other sections, such as Marginal Stars, actually reduce the overall effect when included.
The pattern related to line-initial characters is much weaker and does not explain the global behavior.
Overall, the data suggest that the Voynich manuscript contains long-range sequential structure that is more sensitive to word order than typical natural language texts. At the same time, this structure is concentrated in specific sections rather than evenly distributed.
In contrast, when the same word shuffle is applied to the Voynich text or to Torsten Timm’s generated text, the long-range MI decreases clearly. (I have carried out bootstrap tests and the difference is statistically significant). This behavior has also been noted by Nablator, who reported a similar stability in natural languages and a reduction in Voynich after word shuffle.
To understand where the effect comes from, I repeated the analysis inside the Voynich manuscript:
- by section (Herbal, Biological, Zodiac, etc.)
- by page
- by lines grouped by their initial character
- using leave-one-out tests to see which parts contribute most to the global gap
The results show that the effect is not uniform across the manuscript. The Herbal section accounts for a large share of the global long-range gap. Some other sections, such as Marginal Stars, actually reduce the overall effect when included.
The pattern related to line-initial characters is much weaker and does not explain the global behavior.
Overall, the data suggest that the Voynich manuscript contains long-range sequential structure that is more sensitive to word order than typical natural language texts. At the same time, this structure is concentrated in specific sections rather than evenly distributed.
| Corpus / Subset | Tokens | Internal tail gap | Change in global gap if removed | Meaning |
|---|---|---|---|---|
| Natural languages (control) | varies | ~0 to 1e-5 | — | Word shuffle has almost no effect on long-range MI |
| Voynich (global) | 38262 | 0.001222 | — | Clear reduction after word shuffle |
| Voynich – Herbal | 10928 | 0.001198 | -0.000204 | Strong driver of the global gap |
| Voynich – Biological (balneological) | 6327 | 0.000402 | -0.000049 | Small positive contribution to global gap |
| Voynich – Cosmological | 2246 | ~0.00117 (global without it) | -0.000052 | Minor contributor |
| Voynich – Marginal stars only | 11646 | 0.000810 | +0.000209 | Reduces the global gap (diluting effect) |
| Voynich – Lines starting with "o" | 6700 | 0.001188 | Very small effect | Strong internal structure, but not a main driver |
| Voynich – Lines starting with "d" | 5819 | 0.000816 | Near zero effect | Moderate internal structure, minimal global impact |
| Torsten Timm (generated text) | varies | ~0.002–0.003 | — | Strong reduction after word shuffle |
![[+]](https://www.voynich.ninja/images/collapse_collapsed.png)