• Ciphered Latin/Hebrew/Italian texts (various substitution styles)
  
• Real languages reformatted to look Voynich-like
  
• Constructed languages with invented grammar
  
• Structured pseudo-languages (rules for prefixes/suffixes, but no meaning)
  
• Shorthand/abbreviation systems treated as full glyph sets
  

• Source corpora: medieval Latin, Italian, Occitan, Hebrew (public domain).
  
• Ciphers to implement:
  
  1. Simple monoalphabetic substitution
     
  2. Homophonic substitution (n symbols per plaintext char)
     
  3. Syllabic substitution (map digraphs/triagraphs)
     
  4. Nomenclator (frequent words → special symbols)
     
• Extras: line-initial/line-final rules, abbreviation expansion (Latin breviographs), occasional nulls.
  

• Rewrap real Latin/Italian texts into Voynich-like lines/paragraphs and enforce a few layout quirks (e.g., frequent “q-” line-initial tokens) to probe the effect of mise-en-page alone.
  

• Generators:
  • Finite-state morphology (prefix–stem–suffix classes).
    
  • PCFG (probabilistic context-free grammar) with phonotactics.
    
• Dialects: Currier-A/B style parameter shifts (suffix set, token length).
  

• Automata with rules like:
  • prefix ∈ {qo, q, o, ch, …}, stem ∈ Σ{a,i,e,o,y}, suffix ∈ {dy, n, in, ain, aiin, …}
    
  • position-dependent variants (line-initial gets more “q”)
    
  • tunable affix productivity, stem entropy, and run-lengths
    
• Also include a human-plausible generator: Markov/HMM with simple constraints to simulate “fast fluent scribbling.”
  

• Start with Latin prose; compress using a learned set of ~30–60 breviographs/abbreviations (e.g., -us, -rum, -que), then hide the mapping (treat abbreviographs as glyphs). Vary aggressiveness.
  

• Zipf slope & curvature; Heaps’ law α, K
  
• Word-length distribution; KS/AD distance vs VM
  
• Vocabulary growth by page/quire
  

• Char bigram/trigram distributions; JS divergence
  
• Conditional entropy H(Xₙ|Xₙ₋₁) and H(wordₙ|wordₙ₋₁)
  
• Mutual information vs distance (Hilberg-style curve)
  

• Morfessor/BPE: number of subunits, affix productivity, stem/affix ratio
  
• Family proliferation: counts of {dain, daiin, daiiin}-type ladders
  

• Line-initial vs line-final token profiles
  
• Paragraph-initial bias
  
• Page/section (Herbal/Astro/Balne/Recipes) drift metrics (KL divergence)
  

• LZMA/PPM/ZPAQ ratios
  
• n-gram perplexity (n=3..7) trained on one half, tested on the other
  
• Tiny Transformer perplexity (character-level) trained on each universe, tested on VM (cross-perplexity)
  

• UMAP/t-SNE on character n-gram vectors; silhouette vs VM cluster
  
• Rank-order correlation (Kendall τ) of frequency lists
  

• Distance aggregation: Normalize each metric to z-scores, then compute a weighted composite distance of each synthetic to the VM. Rank universes by median distance.
  
• Model selection: Approximate Bayesian Computation (ABC): treat generator knobs as priors, accept parameter settings whose synthetic stats fall within ε of the VM. Compare posterior mass across universes.
  
• Held-out validation: Fit knobs on half the VM; test distances on the other half (and per section).
  

• Ablations: remove line-position rules or suffix ladders—does fit collapse?
  
• Overfitting guard: ensure no generator is trained directly on VM tokens (only statistics), and verify generalization across sections.
  
• Adversarial baseline: try to force ciphered Latin to match VM—if it still lags pseudo-language on multiple metrics, that’s strong evidence.
  

• Python: 
  numpy, pandas, scikit-learn, matplotlib, networkx
  
• NLP/stat: 
  morfessor, sentencepiece (BPE), nltk for n-grams
  
• Compressors: builtin, lzma, bz2, zlib; optional PPMd via a Python wrapper
  
• Dimensionality reduction: 
  umap-learn, scikit-learn, (t-SNE/UMAP)
  
• Lightweight Transformers (optional): 
  transformers with a tiny char-LM
  

• A league table: per-universe composite distance to VM (with error bars)
  
• Posterior plots: which parameter regions (e.g., high suffix productivity, low stem entropy) best match VM
  
• Confusion matrix from a classifier trained to tell universes apart using the fingerprint; if VM gets classified as “structured pseudo-language” with high confidence, that’s decisive.
  

• Implement Universe D (pseudo-language) and Universe A(1) (mono-substitution over Latin).
  
• Compute a mini fingerprint: Zipf slope, word-length KS, bigram JS, compression, Morfessor affix productivity.
  
• Generate 100 synthetics for each universe; plot distance distributions vs VM.
  
• If pseudo beats ciphered Latin on 4/5 metrics, you’ve got a publishable note.
  

• Layout leakage: VM line/page structure matters—always replicate it in synthetics.
  
• Cherry-picking metrics: pre-register the metric set; report all.
  
• Over-tuning: do ABC on one half; evaluate on the other.
  
• Section bias: score by section and overall; the winner should be consistent.
  

(09-09-2025, 03:34 PM)Phoenixjn Wrote: You are not allowed to view links. Register or Login to view.Does this kind of “synthetic benchmarking” sound worth trying?

(09-09-2025, 05:03 PM)oshfdk Wrote: You are not allowed to view links. Register or Login to view.

(09-09-2025, 03:34 PM)Phoenixjn Wrote: You are not allowed to view links. Register or Login to view.Does this kind of “synthetic benchmarking” sound worth trying?

No, at least not to me. Many reasons, but the primary ones are:

1) Modern AIs appear to be all-knowing universal systems that can reliably produce high quality content across many domains, but they can't and they won't.

2) Moreover, even if this experiment was possible and we got a magic machine that would produce, say, 100 enciphered Latin herbals, and 100 enciphered Arabic herbals, and 100 pseudo language herbals, and we got lucky and found out that according to statistical metric A the Voynich MS is 80% Latin herbal, 10% Arabic herbal and 10% pseudo language, what are we going to do with this information? I see absolutely no use for it. You certainly won't be able to exclude that the Voynich MS is X or Y based on the similarity argument, the maximum you can say it's not very similar to X or Y. It still could be X or Y, just an unusual specimen of X or Y. So, no actual useful information.

(10-09-2025, 12:54 AM)Phoenixjn Wrote: You are not allowed to view links. Register or Login to view.Or, now or in the future, we do generate thousands of fake texts reliably with the help of AI, the tests do work, and they do consistently and repeatably pin the VMS with high confidence to a particular universe of texts.

(10-09-2025, 12:54 AM)Phoenixjn Wrote: You are not allowed to view links. Register or Login to view.ChatGPT 5 is already a PhD expert in everything related to language (and math/stats/physics).