Published May 17, 2026 | Version v2
Preprint Open

The Decipherment of the Voynich Manuscript_ Identification as a Medieval Compendium of Alchemy

Authors/Creators

  • 1. Independent Psychology Researcher
  • 2. Independent Mathematics Researcher/Scholar

Description

By applying the proprietary "OI-2026 Protocol," a method based on structural elucidation, to the Voynich manuscript—which has remained undeciphered for approximately 600 years—we systematically extracted its distributional correspondences with a corpus of medieval Latin medical and alchemical literature (Stream B). For 9,733 words (99.4% of the total 9,783 slots) that passed the dual constraints of contextual similarity in a multi-dimensional space and functional roles (equivalent to parts of speech) automatically classified from the geometric arrangement of the manuscript, corresponding candidates within the Latin corpus were identified. In this paper, these correspondences are referred to as "distributional translation candidates." This does not constitute a definitive semantic translation, but rather provides primary source material for subsequent historical and herbalistic validation. Furthermore, a strong positive correlation (R=0.7080) was observed between the endpoints of branches and leaves in the illustrations and the occurrence rate of "material-type" vocabulary in the text, suggesting that the manuscript is a structured recording medium in terms of both text and iconography. These observations statistically support the possibility that the manuscript is a recording system closely related to the traditions of 16th-century alchemy and early medicine. 

 

Files

Statistical Domain Identification of the Voynich Manuscript_ Distributional Mapping to 16th-Century Latin Alchemical and Medical Vocabulary (日英併記版)Version 1.1.0.pdf

Files (33.0 MB)

Name Size Download all
md5:4c3de93618b4e94f114ce647f643574f
971.0 kB Preview Download
md5:6d0e16054211fd73d92b73f81356407c
27.5 MB Preview Download
md5:20305b4b3296f38bab7f2557db69182f
1.2 kB Preview Download
md5:776b1e891394a6855ee692188efbbae9
8.7 kB Preview Download
md5:6c67dfe45bb98b76242b86ca81d508b4
456 Bytes Preview Download
md5:6da2bc8b4d0fa5a191247f620aaf4ff8
414.6 kB Preview Download
md5:5804afd10dce220567f205fefbbdc8df
898 Bytes Preview Download
md5:526c9e462145add674f3497fd9818bc7
321.9 kB Preview Download
md5:ac1349d043104e7bd25e0c79906bf447
1.1 MB Preview Download
md5:31b39435a68cb429691c8787d308d8cb
913.3 kB Preview Download
md5:0512f2a85521097b9a12d02ed1c2a70c
5.5 kB Preview Download
md5:3cbe0c2c4db96a7c880435244781915c
1.5 MB Preview Download
md5:8e8dc92b1231cd1ddb5ca6ccb93e8170
231.2 kB Preview Download
md5:64730746d93919682ed0b3fa4cf20862
5.8 kB Preview Download
md5:71b95b649273a55753e73e3b4153dd0b
6.2 kB Preview Download
md5:f69ca72abc7011915aaa7d9fa20b467f
7.1 kB Preview Download
md5:fb9f332233dc2505fa39e5962f5e5381
6.4 kB Preview Download
md5:a7d7284a54f69a3afdb105c6fa116282
4.5 kB Preview Download
md5:b3b24e19da5487525d070cbc06827365
6.5 kB Preview Download
md5:eded6bfda7bf1e37d18b92842102ff02
6.2 kB Preview Download
md5:28844b32f4f09173c53078905bf4aff7
10.1 kB Preview Download
md5:624c26bca629ad942d25864a209e2a9c
9.1 kB Preview Download
md5:4066e6e37ee6b0a71ef457aefea0b8de
7.3 kB Preview Download

Additional details

Related works

Continues
Publication: 10.5281/zenodo.19574356 (DOI)