18-02-2026, 07:14 PM
Hi everyone,
I’ve been following the discussions here for a while regarding the rigid morphology of Voynich "words". I approached MS 408 not as a linguist, but from a Forensic Engineering perspective.
My hypothesis was simple: What if the rigidity isn't grammatical, but mechanical?
After mapping the transition probabilities of 35,000 tokens, I have isolated the hardware architecture responsible for generating the text. I call it The Syntaxis Volvella.
![[Image: default.png]](https://zenodo.org/api/iiif/record:18684047:stator_rotor.png/full/!800,800/0/default.png)
I am sharing my findings here because I need this community's critical eye on the data.
1. The Architecture: A 17:13 Differential. The statistical "dead zones" in the text suggest a stator disk with 17 Semantic Sectors (providing the Suffix/Context) interacting with a planetary rotor of 13 teeth (providing the Stem/Root). This specific 17:13 ratio explains the cyclical repetition and the "State-Memory" transitions that purely linguistic models fail to predict.
Volvella architecture JSON:
2. The QOK Anomaly. This is the strongest physical evidence. In my telemetry analysis, the token QOK is not a word, it’s a mechanical synchronization artifact. It appears with statistical significance at exactly 120-degree intervals on the stator (Sectors equivalent to EEDY, AIIN, EY). This phase-lock strongly implies the internal rotor is driven by a 3-Lobe Triangular Cam.
![[Image: default.png]](https://zenodo.org/api/iiif/record:18684047:Fig2_QOK_Synchronization.png/full/!800,800/0/default.png)
3. The Turing Test (Simulation Results) I wrote a Python script to simulate this hardware. I fed it zero linguistic rules—only the physical constraints of the gears and the probability matrix of the sectors.
Result: The synthetic text matches the real MS 408 with a 99.6% Zipf Law correlation.
![[Image: default.png]](https://zenodo.org/api/iiif/record:18684047:Fig1_Zipf_Comparison.png/full/!800,800/0/default.png)
The complex "language" behavior is actually just the friction and geometry of a machine.
The Paper & Data: You are not allowed to view links. Register or Login to view.
I have uploaded the full breakdown, the Python code, and the CSV telemetry logs to Zenodo. I invite you to audit my code and the "Hard Lock" tables.
I am not claiming to have translated the meaning YET, but I believe I have successfully reverse-engineered the source. I would appreciate your thoughts!!
Regards
Steven Quevedo
I’ve been following the discussions here for a while regarding the rigid morphology of Voynich "words". I approached MS 408 not as a linguist, but from a Forensic Engineering perspective.
My hypothesis was simple: What if the rigidity isn't grammatical, but mechanical?
After mapping the transition probabilities of 35,000 tokens, I have isolated the hardware architecture responsible for generating the text. I call it The Syntaxis Volvella.
I am sharing my findings here because I need this community's critical eye on the data.
1. The Architecture: A 17:13 Differential. The statistical "dead zones" in the text suggest a stator disk with 17 Semantic Sectors (providing the Suffix/Context) interacting with a planetary rotor of 13 teeth (providing the Stem/Root). This specific 17:13 ratio explains the cyclical repetition and the "State-Memory" transitions that purely linguistic models fail to predict.
Volvella architecture JSON:
Code:
{
"artifact_designation": "V-2206 Syntaxis Volvella",
"theoretical_basis": "Mechanical Generative System (Non-Linguistic)",
"architecture": {
"stator_unit": {
"description": "Outer fixed disk defining the semantic context (Suffixes)",
"segments": 17,
"sector_topology": [
{"id": "AIIN", "function": "ITEM_LISTING", "mechanical_bias": "High_D_Lock"},
{"id": "IIN", "function": "GENERIC", "mechanical_bias": "None"},
{"id": "IN", "function": "GENERIC", "mechanical_bias": "None"},
{"id": "EEDY", "function": "SYNC_NODE_1", "mechanical_bias": "Cam_Lobe_Contact (QOK)"},
{"id": "HEDY", "function": "PROCESS_DESCRIPTOR", "mechanical_bias": "Hard_C_Lock"},
{"id": "EDY", "function": "SYNC_NODE_2", "mechanical_bias": "Cam_Lobe_Contact (QOK)"},
{"id": "DY", "function": "VARIABLE_INPUT", "mechanical_bias": "Low_Friction"},
{"id": "AM", "function": "GENERIC", "mechanical_bias": "None"},
{"id": "OM", "function": "GENERIC", "mechanical_bias": "None"},
{"id": "OS", "function": "DATA", "mechanical_bias": "CHE_Bias"},
{"id": "US", "function": "GENERIC", "mechanical_bias": "None"},
{"id": "AL", "function": "NULL_SEPARATOR", "mechanical_bias": "Empty_Stem"},
{"id": "AR", "function": "NULL_TERMINATOR", "mechanical_bias": "Empty_Stem"},
{"id": "OL", "function": "NULL_SEPARATOR", "mechanical_bias": "Empty_Stem"},
{"id": "OR", "function": "NULL_TERMINATOR", "mechanical_bias": "Empty_Stem"},
{"id": "EY", "function": "SYNC_NODE_3", "mechanical_bias": "Cam_Lobe_Contact (CH)"},
{"id": "KY", "function": "FRICTION_ZONE", "mechanical_bias": "QO_Bias"}
]
},
"rotor_unit": {
"description": "Inner planetary gear with 3-Lobe Cam geometry",
"gear_ratio_stator_to_rotor": "17:13",
"core_lexicon": ["QOK", "CH", "SH", "OK", "D", "S", "C"],
"synchronization": {
"cam_profile": "Triangular (Eccentric)",
"phase_alignment": ["EEDY", "AIIN", "EY"]
}
},
"interface_unit": {
"description": "Radial Alidade with 3 sighting windows",
"modes": {
"NORTH_WINDOW": {"trigger": ["P", "F"], "content_pool": "RING_A (Consonants)"},
"SOUTH_WINDOW": {"trigger": ["T", "K"], "content_pool": "RING_B (Vowels)"},
"NEUTRAL_WINDOW": {"trigger": "NONE", "content_pool": "RING_C (Rotor)", "usage": 0.85}
}
},
"rings_content": {
"RING_A": {
"description": "Consonants / Hard prefixes (accessed by NORTH_WINDOW)",
"top_teeth_freq": [
["CH", 0.52],
["C", 0.20],
["CHE", 0.12],
["SH", 0.08],
["OL", 0.08]
]
},
"RING_B": {
"description": "Vowels / Soft connectors (accessed by SOUTH_WINDOW)",
"top_teeth_freq": [
["CH", 0.30],
["E", 0.22],
["C", 0.19],
["A", 0.08],
["EE", 0.07]
]
},
"RING_C": {
"description": "Rotor core stems (accessed by NEUTRAL_WINDOW)",
"top_teeth_freq": [
["QOK", 0.24],
["D", 0.23],
["CH", 0.21],
["S", 0.14],
["OK", 0.13]
]
}
}
},
"operational_physics": {
"batch_processing_inertia": {
"description": "Operator tends to stay in the same sector group (data batching)",
"mean_sector_jump": 5.71,
"median_sector_jump": 5.0,
"percentage_repeat_sector": 13.43
},
"stochastic_emission": "Output = P(Stem|Sector) * P(Sector_t+1|Sector_t)",
"mechanical_rigidity_examples": [
{"sector": "HEDY", "forced_tooth": "C", "observed_frequency": 0.30},
{"sector": "AIIN", "forced_tooth": "D", "observed_frequency": 0.30},
{"sector": "EY", "preferred_tooth": "CH", "observed_frequency": 0.16},
{"sector": "EEDY", "preferred_tooth": "QOK", "observed_frequency": 0.17}
]
}
}2. The QOK Anomaly. This is the strongest physical evidence. In my telemetry analysis, the token QOK is not a word, it’s a mechanical synchronization artifact. It appears with statistical significance at exactly 120-degree intervals on the stator (Sectors equivalent to EEDY, AIIN, EY). This phase-lock strongly implies the internal rotor is driven by a 3-Lobe Triangular Cam.
3. The Turing Test (Simulation Results) I wrote a Python script to simulate this hardware. I fed it zero linguistic rules—only the physical constraints of the gears and the probability matrix of the sectors.
Result: The synthetic text matches the real MS 408 with a 99.6% Zipf Law correlation.
The complex "language" behavior is actually just the friction and geometry of a machine.
The Paper & Data: You are not allowed to view links. Register or Login to view.
I have uploaded the full breakdown, the Python code, and the CSV telemetry logs to Zenodo. I invite you to audit my code and the "Hard Lock" tables.
I am not claiming to have translated the meaning YET, but I believe I have successfully reverse-engineered the source. I would appreciate your thoughts!!
Regards
Steven Quevedo