Hello,

I’ve been exploring the Voynich manuscript and believe I may have found a consistent linguistic pattern worth further study. I’m writing to see if anyone with more expertise in Voynich studies, medieval Hebrew, or manuscript linguistics might be interested in reviewing it or collaborating.

The core hypothesis is this: the Voynich script represents a form of Hebrew, but written in mirror — as though the author wrote right-to-left while looking into a mirror. I'm not fluent in Hebrew, but when I used AI to help with my theory it started to yield some results. When you reverse both the word order and the glyphs, and then map EVA transcriptions to Hebrew letters, a surprisingly coherent and repeatable pattern emerges.

I’ve tested this on several folios (including f11r, f14r, and f33r). After decoding, many roots resemble known Hebrew words used in medieval herbal and ritual texts — particularly those found in Sefer Refu'ot and related manuscripts. Words like:

לוחת (stir/mix)

שפח (sprinkle)

נייד / ניד (dissolve/crush)

דוה / רוה (flow, soak)

שקל / מדד (weigh/measure)


I've also done root frequency analysis using a basic script, and it shows consistent, plausible Hebrew roots with semantic relevance to the illustrations (e.g., herbal processes).

I'm not claiming to have “solved” the manuscript — just that this mirror-Hebrew decoding method yields unusually structured, linguistically meaningful results that don’t appear random, and I’d be eager to hear others’ thoughts or critiques.

The Voynich puzzle
A new game, the campaign on gamefond will start soon.

You are not allowed to view links. Register or Login to view.
You are not allowed to view links. Register or Login to view.

Dear Voynich Ninja community,

As you might know, I’ve been working on training LLM Large Language Models (GPT-like models) on Voynich EVA transliterations. This is not about using ChatGPT, but about training language models from scratch using only Voynich EVA text.

I’m aware that GPT models are a sort of black box, and it’s often hard to understand the mechanisms they use to “learn” patterns. In this project, I’ve tried to explore how the GPT model makes predictions — to gain some intuition into the decision-making process.

Let me first introduce the key concepts I’ve been working with:

• Loss: Loss is a measure of how wrong the model's predictions are compared to the actual next word. In language models, it's typically cross-entropy loss, which penalizes the model more when it assigns low probability to the correct word. A lower loss means the model is better at predicting the next token given its context.
  
• Prediction: The prediction is the model’s guess for the next word in a sequence. For example, given a context of 4 tokens (block_size = 4), the model looks at those 4 tokens and outputs a probability distribution over the vocabulary, selecting the most likely next token.
  
• Saliency: Saliency refers to how much each input token contributes to the model’s prediction. If we use a block_size of 4, saliency tells us which of the 4 previous tokens had the most influence on predicting the next word. For example, in the sequence ["the", "brown", "cat", "sat"] → ?, the model might predict "on". Saliency would then indicate how important each of the previous tokens was in making that prediction. Tokens with higher saliency are considered more influential.
  

• I split the text into 5 segments. For each fold, I used 80% of the data for training and 20% for validation, rotating through all segments.
  
• This way, I could generate predictions for the entire corpus.
  

• Loss: represented by the border thickness — thicker means higher loss.
  
• Saliency: represented by the background color intensity — darker means higher saliency. Since each word is part of 4 prediction contexts (due to block_size = 4), saliency here is averaged over those 4 instances.
  
• Prediction probability: represented by border color — green for high confidence, red for low. The predicted probabilities are generally low, but this is also the case when training GPT on small corpora like a single book, even in natural languages.
  

[size=1][/size]

• context_0 to context_3: the 4 previous tokens used as input (the model's context).
  
• target: the real next word in the sequence.
  
• pred_word: the word predicted by the model.
  

• green: when pred_word == target
  
• yellow: similar words according to LevenShtein similarity (>0.5)
  

Just to highlight this interesting post and give it its own thread:

(13-07-2025, 08:34 PM)magnesium wrote: You are not allowed to view links. Register or Login to view.I know this is slightly off-topic, but as we're poking around digitized Swiss archives: Koen, if you haven't seen it already, this manuscript is an extremely good reference for the handwriting of the You are not allowed to view links. Register or Login to view. and You are not allowed to view links. Register or Login to view. marginalia: You are not allowed to view links. Register or Login to view.

I'm not making any definitive claims here, but I wanted to point out a superficial resemblance between the 16-wedge circle on You are not allowed to view links. Register or Login to view. and this circular diagram of the 16 geomantic figures in the following 15th-century divination/astrology manual:

St. Gallen, Stiftsbibliothek, Cod. Sang. 756: Composite manuscript on geomancy, chiromancy, iatromathematics, astronomy, alchemy and medicine (You are not allowed to view links. Register or Login to view.).



The You are not allowed to view links. Register or Login to view. circle, for reference:



The 16-wedge subdivision of the circle and the central floral motif stood out to me. However, the most glaring difference between the two is obviously that the figures themselves are missing from f69r. It's piling speculation on speculation, but I have half a mind that one of the VMS authors saw something like the Cod. Sang. 756 figure, had no knowledge of geomancy, and then either tried to draw something similar from memory or verbally described the figure to the illustrator.

Hello everyone,
I’m a newcomer to Voynich studies, but I’d like to share a hypothesis I developed after closely examining publicly available images of the manuscript. Please keep in mind I’m not a specialist — this is just an idea from an outsider's perspective — but I’d sincerely appreciate any feedback.
My hypothesis is that the manuscript may be centered around beekeeping, with symbolic and possibly sacred aspects attached to bees.
Here’s a brief summary of my reasoning:

• The botanical pages could represent either plants grown near beehives or flowers favored by bees for nectar. In some drawings, the roots seem exaggerated — possibly indicating how “attractive” the plant is to bees (e.g., stronger roots = more nectar?).
  
• Many of the female figures might represent bees — particularly worker bees or queens — often depicted immersed in fluid, holding objects (perhaps pollen or tools), or emerging from pipes (perhaps hive entrances).
  
• The spiral or rosette diagrams might be stylized cross-sections of hives, showing their inner structure or seasonal changes.
  
• The astronomical sections might represent the cycle of queen production, honey storage patterns, or symbolic relationships between bees and celestial patterns — such as star positions during swarming.
  
• The lack of realistic depiction in many plants could reflect a bee’s visual world (colors from above vs. below, petal symmetry, etc.), not a human herbalist’s.
  

• Why some women are crowned or veiled (perhaps symbolizing queens),
  
• Why the text is written so carefully (a sacred or secret manuscript about bees?),
  
• Why there is only one copy (a ritual or private use?).
  

Dear Ninja Team,

My name is Hakan Adıbelli, and I’ve been conducting an independent deep-structure analysis of the Voynich Manuscript based on alchemical logic, Fibonacci sequencing, and symbolic fusion cycles across the folios.
I believe I may have uncovered a key principle underlying the manuscript’s structure:
namely, that each plant illustration represents not a singular species, but a fusion blueprint of multiple botanical and symbolic elements — constructed in harmonic cycles and encoded across pages via Fibonacci-derived cipher blocks.
Further, I’ve come to realize that the final diagrams of the manuscript likely represent an “end state” — a symbolic interface or mechanical construct — which must be reverse-engineered to decipher earlier folios. In this view, the manuscript is not meant to be “read” linearly, but “reconstructed” structurally.

The full exploration of this hypothesis is captured in my ongoing dialogue with an AI-based assistant (GPT), which has helped me formulate and refine these ideas. The original discussion is in Dutch, but I believe its insights may be of interest to your research team or broader Voynich scholars.

➤ You can access the full discussion (with live hypotheses, cipher parsing, and layered logic) here:
You are not allowed to view links. Register or Login to view.

I understand the document is currently in Dutch, and I am in the process of translating it into English for broader academic use. If your team would be interested in early access to this material, or would be willing to assist in its validation or dissemination, I would greatly appreciate the opportunity to collaborate or contribute to ongoing Voynich research.
Thank you for preserving and opening access to this enigmatic treasure. I hope this contribution may help shed further light on its mystery.

Warm regards,
Hakan Adıbelli
belli187@hotmail.com
Venlo, The Netherlands

After a long process of sometimes difficult elimination, we have democratically determined our four favorite plants. Now, the time has come to choose a winner.

Since I want to announce the results on Voynich Manuscript Day, I decided to use a Google form this time. Like that, I can collect the answers and only reveal them on the big day. There's a line where you can provide your forum name. I will treat all answers anonymously, but providing your forum name will help to ensure voting is proceeding normally. You may also write why you voted for that particular plant, which will give me something to talk about when I announce the results

Anyway, without further ado, these are the finalists. You are not allowed to view links. Register or Login to view.

   

Is there a possibility to trace the VMs materials origins ? Did anyone every tried to match the manuscripts style of making with any of the contemporary places that made books ? Was chemical analysis of the VMs materials ever performed ? 
Now that we know that Karl Wideman could likely be the person who sold the VM to Rudolf II., what if we could actually find similarly made books somewhere around southern germany or northern italy ?

I'm wondering if anyone would be willing to help me with my Voynich decoding theory by making a blind test to see if I'm being biased by the images while attempting to translate using my method. 

If someone could get me an EVA transcription of one of the balneological folios, separated by line and paragraph, as well as the labels simply labeled "Label 1, label 2, etc" then I could test whether my translation method is accurate even without being biased by the images.

So far I've only attempted translating f77r, so any folio that's not that I wouldn't recognize.

For example, for f77r, I'd want something like

"Label 1: dorchdar
Label 2: dotedy
...

Paragraph 1:
Line 1: poldarair ol qokol chey ...
Line 2: tolchdar shey qotaiin...

Paragraph 2: 
Line 1: otedy qokor shedy..."

I would do it myself but I don't want to bias myself by associating the images with the text before I translate it