I was wondering... what happens with different transliterations? So far I was using EVA. But EVA splits many glyphs into very fine units and ends up with a large alphabet. Currier, on the other hand, uses a much smaller set of symbols and groups elements that EVA keeps separate. Torsten Timm style text also works with a relatively compact alphabet based on EVA (there are not special glyphs, making the amount of used characters quite simmilar to a latin alphabet).
If the long-range MI gap were mainly an artifact of rare glyphs or over-fragmentation, reducing the alphabet should weaken the effect (this is at least the logical thing: it should behave more like natural languages and reduce the gap, isn't it?).
Instead, I observe the opposite...
With the same raw vs shuffled comparison:
Summary:
The Currier gap is more than twice the EVA gap.
This is not what I would expect if the signal were coming from rare or decorative glyphs. When the alphabet is reduced and tokens are grouped more coarsely, the long-range structure does not disappear. It becomes stronger.
At the section level, the same pattern seen in EVA still holds in Currier. Some sections contribute strongly to the global gap, while others dilute it. The effect is not uniformly distributed across the manuscript.
The leave-one-out tests confirm that removing certain sections changes the global gap in a measurable way. This again indicates that the structure is not evenly spread across the text.
Below is a simplified comparison of the strongest section-level effects under EVA and Currier.
Line-initial groupings show a weaker effect overall, but still reflect uneven distribution.
In both transliterations, section-level grouping matters more than line-initial grouping. The structure appears to be organized at a broader textual scale rather than driven by local line mechanics.
What could this mean?
One explanation is statistical. A smaller alphabet reduces sparsity and improves estimation of mutual information. That alone could amplify the measured gap.
Another explanation is structural. EVA may over-segment the text. If meaningful units are split into smaller pieces, long-range dependencies get diluted. Currier, by grouping more tightly, may better reflect the functional units that carry sequential constraints.
The important point is that the long-range MI effect is robust under transliteration changes. It does not depend on the large EVA glyph inventory. And in a compact representation, it becomes comparable in magnitude to a known sequential generative model such as Torsten Timm.
If the long-range MI gap were mainly an artifact of rare glyphs or over-fragmentation, reducing the alphabet should weaken the effect (this is at least the logical thing: it should behave more like natural languages and reduce the gap, isn't it?).
Instead, I observe the opposite...
With the same raw vs shuffled comparison:
- EVA shows a modest long-range gap.
- Currier shows a much larger one.
- Torsten Timm style generated text falls in a similar range to Currier.
Summary:
| System | Tokens | Alphabet size | Tail MI gap |
|---|---|---|---|
| EVA | ~38,000 | 116 | 0.00122 |
| Currier | ~15,000 | 36 | 0.00269 |
| Torsten Timm | ~11,000 | 20 | ~0.002–0.003 |
The Currier gap is more than twice the EVA gap.
This is not what I would expect if the signal were coming from rare or decorative glyphs. When the alphabet is reduced and tokens are grouped more coarsely, the long-range structure does not disappear. It becomes stronger.
At the section level, the same pattern seen in EVA still holds in Currier. Some sections contribute strongly to the global gap, while others dilute it. The effect is not uniformly distributed across the manuscript.
The leave-one-out tests confirm that removing certain sections changes the global gap in a measurable way. This again indicates that the structure is not evenly spread across the text.
Below is a simplified comparison of the strongest section-level effects under EVA and Currier.
| Grouping | EVA tail gap (approx) | Currier tail gap (approx) | Effect |
|---|---|---|---|
| Global baseline | 0.00122 | 0.00269 | Currier > EVA |
| Herbal section | High contributor | High contributor | Strong in both |
| Marginal / stars only | Dilutes gap | Dilutes gap | Weak structure |
| Biological | Moderate | Moderate | Section-dependent |
Line-initial groupings show a weaker effect overall, but still reflect uneven distribution.
| Grouping | EVA pattern | Currier pattern |
|---|---|---|
| Lines starting with common glyphs | Small variation | Small variation |
| Rare initials | Minimal impact | Minimal impact |
In both transliterations, section-level grouping matters more than line-initial grouping. The structure appears to be organized at a broader textual scale rather than driven by local line mechanics.
What could this mean?
One explanation is statistical. A smaller alphabet reduces sparsity and improves estimation of mutual information. That alone could amplify the measured gap.
Another explanation is structural. EVA may over-segment the text. If meaningful units are split into smaller pieces, long-range dependencies get diluted. Currier, by grouping more tightly, may better reflect the functional units that carry sequential constraints.
The important point is that the long-range MI effect is robust under transliteration changes. It does not depend on the large EVA glyph inventory. And in a compact representation, it becomes comparable in magnitude to a known sequential generative model such as Torsten Timm.
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