Dear Voynich Manuscript Researchers,
My name is Keishi Oi, an independent researcher based in Japan. I am writing to share the mathematical proof and the decryption methodology of the Voynich Manuscript, which I have named the "OI-2026 Dismantling Protocol."
Core Finding:
The Voynich Manuscript is not written in a natural language, nor is it a simple substitution cipher. Through mathematical modeling, I have proven that the text is a "Combinatorial Data Matrix" — a broadly defined artificial language (comparable to modern Data Description Languages) generated by a deterministic automaton.
Evidence & Data:
The complete mathematical proof, including the calculation of the 11.05 bits word entropy, the extraction of the 17x73 primary register map, and the 100% error-free syntax parsing of the entire combinatorial space, has been made open access.
You can download the full paper (PDF) and the dataset via Zenodo here:
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(Note: The full manuscript has also been submitted to Cryptologia and is currently under peer review.)
I believe the OI-2026 protocol mathematically answers many of the statistical anomalies (such as the extreme Zipf's law deviations) that this community has long debated.
I welcome your rigorous verification, mathematical critiques, and open discussions.
Regarding the methodology and the use of AI:
I am fully aware of the community's concerns regarding LLM-generated "slop." I wish to clarify that I utilized AI strictly as a tool for
Python script generation and English translation to facilitate international dissemination of my findings.
Crucially, the core discovery — the 17x73 register map and the four-stage logic architecture — was
NOT generated by AI. These structures were identified through deterministic mathematical analysis and verified in independent Python environments (Google Colab/VSCode) to prevent any LLM hallucinations.
Furthermore, please understand that the modern IT terminology I employ (such as "OS," "registers," "Boot," and "Termination") is intended strictly as a
functional metaphor. These terms are used to translate the impersonal, abstract behaviors of the manuscript's combinatorial data structure into a format that is comprehensible to human researchers.
If you suspect this is "AI slop," I invite you to stop judging by the style and start judging by the data. The methodology is fully disclosed, and the dataset is public. I welcome any researcher to run the code on the Zenodo repository and attempt to falsify the results through direct replication.
Best regards,
Keishi Oi
Independent Researcher