Structural Investigation, Beinecke MS 408, Not Deciphered

Constraining how the Voynich manuscript was written without claiming what it says.

I directed the team (the AI coding and research agents I direct) to test published statistical claims, paleographic transcriptions, scan-aligned word geometry, and candidate production mechanisms on the Voynich manuscript (Yale University, Beinecke Rare Book and Manuscript Library, MS 408). The evidence localizes a disciplined, line-bounded two-layer writing system. It does not decipher the text, establish a validated historical production mechanism, or claim first discovery of the underlying paleographic and statistical ingredients.

Epistemic separation

Four categories kept separate on this page

2. Our measured findings & protocol reconciliations

Controlled decompositions and out-of-sample tests across three EVA/v101 transliterations (ZL3b-n, RF1b-e, IT2a-n), 41 held-out folios, and 15 leave-one-quire-out (LOQO) folds across runs s1 to s106 (F-01 to F-47). Our contribution is joint quantitative ablation, exact protocol bridging between external papers and our pipeline, and testing candidate generators and ciphers against the combined multi-scale constraints, not first discovery of the individual ingredients.

3. User-supplied reported evidence (Daybreak lab)

Reported results from the separate Daybreak workspace covering the db-s96 forward-quire tail audit, pharmaceutical label visual/container alignment (s112 to s114), and order-free recipe network comparisons against the Latin Antidotarium Nicolai and medical/culinary controls (s119 to s121). These are reported as negative or constraining findings from that workspace and are kept distinct from directly inspected local run files.

4. Hypotheses & open questions

Why our six-stage line-and-quire structural synthesis fits internal statistics better than plain prose, free gibberish, simple/period/verbose ciphers (F-45, F-47), word-level lexical codes (H-06, weight 2.0 / 3.7), or fixed line-held affix tables (H-14, narrowed to 2.0 / 4.7 under D-067), where our closest synthetic producer (s93, rejected as a unified pass under D-064) still overproduces short-range core recurrence by ~2.6x, and what falsifiable external prediction is needed next.

Interactive protocol & ablation workbench

Test the protocol transformations and slot ablations yourself

Use the two controls below to inspect how external preprocessing choices change cross-boundary mutual information (s38, s51) and what happens to within-line word-length rhythm when operational prefix and suffix frames are preserved or stripped out of sample (s33, s36, s37).

1. Protocol bridge (s38, s51, EXT-RT-2026)

From raw EVA vparse to Rozanova & Temerev (2026)

Select a step to see how each preprocessing choice shifts corpus size and boundary character MI.

Active P-lines / pairs 4,135 lines, 30,342 pairs
1-glyph boundary MI 0.1380 nats (0.1991 bits)
Step delta & alignment Unfiltered ZL3b-n baseline
Voynich 1-glyph boundary (raw bits equiv.) 0.1991 bits
Voynich 2-glyph boundary (S2 -> P2) 0.2449 bits (raw)
Currier B vs Currier A split (s38) B: 0.2380, A: 0.1319 bits
Matched 1,813-pair control (s51): Voynich vs Latin Priapea 0.1599 vs 0.1456 bits (p = 0.38)

Step 0 (Raw vparse): Reads all 4,135 paragraph lines (34,475 tokens, 30,342 adjacent within-line word pairs) in natural log units (0.1380 nats). R&T (2026) report in bits on a filtered, ligature-collapsed subset.

2. Operational slot ablation (s33, s36, s37, EXT-GB-2022)

Where within-line word-length rhythm lives

Toggle the word representation to see how stripping or preserving operational [Prefix, Suffix] frames affects Moran's I and held-out length covariance.

Within-line Moran's I 0.1572 (p = 0.001)
Full-fit intra-line excess +0.0875 (100.0% retained)
Held-out 166 to 41 OOS cov. 100.0% baseline reference
Within-line Moran's I: line-multiset drift vs intra-line ordering excess 0.0698 drift + 0.0875 ordering
Out-of-sample 41-folio length covariance retained (s36, 166 train to 41 test) 100.0% (+0.2801 char^2 full ref.)

Illustrative line segmentation & token lengths under current view:

qokededy8 chars
qotaiin7 chars
chedy5 chars
daiin5 chars
okam4 chars (-m end)

Full Word (P + C + S): Words average 5.20 chars at line start and taper to 4.71 chars at line end. 44.4% of I_within = 0.1572 comes from page/line length drift and 55.6% (+0.0875) from within-line word ordering. Qualification: SlotSegmenter is a descriptive statistical representation fitted on the transliteration, not proof of linguistic morphemes.

Quantitative decompositions & protocol bridges

Four diagrams summarizing our measured findings and their qualifications

Chart showing decomposition of within-line word-length Moran I (0.1572) into 44.4% multiset drift and 55.6% intra-line ordering excess, with 96.0% full-fit and 93.5% out-of-sample collapse under Core_Only frame removal.
Figure 1: Within-line word-length rhythm and operational slot ablation (s33, s36, s37; EXT-GB-2022): Gaskell & Bowern's (2022) stats.py concatenated word lengths across line breaks (w=1). Measuring strictly within lines (also noted in the September 5, 2025 voynich.ninja thread) yields I_within = 0.1572 on ZL3b (44.4% line-multiset drift + 55.6% intra-line ordering excess). Under SlotSegmenter, stripping operational prefixes and suffixes removes 96.0% of full-fit excess and 93.5% of held-out length covariance (166 train to 41 test folios, s36). Qualification: SlotSegmenter is a descriptive representation fitted on the transliteration; in the 90.0% (P, S) cell shuffle, 77% to 79% of tokens sit alone in their in-line (P, S) cell (12% to 13% mutable), and under a fixed morphology segmenter cores retain 48.8% to 54.1% of covariance (L-189, L-190).
Chart showing the 3x3 inter-word pairwise mutual information shuffle-excess grid from word t to word t+1 slots (s34) alongside the 5.65-bit within-word 12-slot coupling constraint (EXT-ZAT-2022, s63, s76).
Figure 2: Inter-word slot coupling (s34) and within-word 12-slot over-generation (EXT-ZAT-2022, s63, s76): Across adjacent words inside a line, positive pairwise MI excess concentrates on the next word's prefix P2 (+0.3171 bits across P1->P2, C1->P2, S1->P2, vs +0.3683 positive-cell sum and +0.3190 signed grid sum; overlapping pairwise MI terms are non-additive). Core-to-core C1->C2 sits -0.0315 bits below within-line shuffle on the full inventory (raw 0.7289 vs shuffled 0.7604) due to short-range core repetition and sparse-inventory bias, not negative MI; at matched size (K=200, s80), common cores carry weak positive sequential order. Within words, Zattera's (2022) 12-slot product has entropy sum 16.71 bits vs word entropy 11.06 bits (5.65-bit coupling, pairwise MI 1.4041 bits, z = +373.4).
Diagram tracing the four-step protocol reconciliation from vparse raw EVA boundary mutual information to Rozanova and Temerev 2026 (+0.1972 and +0.2426 bits), plus s47 TIFF ink-gap checks and s51 matched-sample Latin Priapea overlap.
Figure 3: Exact protocol bridge to Rozanova & Temerev (2026 preprint) and scan-aligned box checks (s38, s45, s47, s51; EXT-RT-2026): Applying R&T's four steps (bits conversion, dropping 80 non-[a-z] lines to leave 28,797 pairs, collapsing 9 EVA ligatures, and subtracting 100 within-line shuffles) reproduces Table 4 and Table 11 of arXiv:2608.17096 to four decimal places (+0.1972 bits 1-glyph, +0.2426 bits 2-glyph). On Beinecke TIFF projections (s45, s47), voynichese.com 3-px SVG grid boxes have R^2 = 0.004 with physical ink gaps, whereas transcription-aligned boxes match certain (.) spaces at kappa = 0.866 and track transition surprisal (rho = +0.168, p = 0.0002), closing O-048 only under s45 box alignment rather than transcription-free segmentation. At 1,813 pairs/sample (s51), Voynich (0.1599 bits) overlaps Latin Priapea (0.1456 bits, p = 0.38).
Diagram comparing the six-stage constrained production hierarchy against tested prose, gibberish, self-citation, grille, and cipher families on 15-quire leave-one-quire-out (LOQO) evaluation.
Figure 4: Constrained six-stage structural synthesis versus tested generator and cipher families (s1 to s106, F-01 to F-47): Each baseline matches one or two projections of the manuscript and fails the joint 15-quire LOQO battery. Line-held affix tables (H-14, narrowed to 2.0 / 4.7 under D-067) reproduce one-step prefix memory out of sample, but their core streams fail LOQO 0/15 (s81) and their conditional lag-2 prefix dependence is ~2.5 to 3x higher than Voynich (10-12/15 quires vs 4-5/15 in s98, F-46). Period ciphers fail 0/12 cells (s102, F-45) and spec-built verbose recipe tables fail 0/18 cells (s104, F-47). Weakened grilles fail LOQO 0/15 (s71). Our closest synthetic producer (s93, rejected as a unified pass under D-064) still overproduces short-range core recurrence by ~2.6x.
Summary of external protocol reconciliations and scope boundaries (Matrix v2, October 18, 2026).
Published Work External Protocol Our Controlled Comparison Measured Outcome & Qualification
Gaskell & Bowern (2022) + stats.py Concatenates ~200-word subsamples across lines into 1D wordlen_bank (w=1); 38 gibberish writers (10,023 tokens). Strictly within-line rook adjacency (30,342 pairs, 4,135 P-lines), 1,000-shuffle null, 166->41 held-out slot ablation (s33, s36, s37, n7; c-168..c-174, c-355). Within-line I = 0.1572 splits into 44.4% multiset drift + 55.6% intra-line ordering; Core_Only(C) removes 96.0% full-fit / 93.5% OOS covariance. Gibberish pool has 38 writers (64 sidecars include variants/pools) and is size-dependent (n7).
Rozanova & Temerev (2026 preprint) Drops 80 non-[a-z] P-lines (28,797 pairs), collapses 9 EVA ligatures, subtracts 100 shuffles in bits; uses voynichese.com 3-px SVG boxes. 4-step bridge (s38), Beinecke TIFF ink-gap projections on box-aligned tokens (s45, s47), matched 1,813-pair Latin subsampling (s51; c-368, c-384..c-387, L-139). Reproduces +0.1972 (1-glyph) and +0.2426 bits (2-glyph) to 4 decimals. SVG boxes have R^2 = 0.004 vs TIFF ink gaps (kappa = 0.866 with certain spaces). At 1,813 pairs, Voynich (0.1599 bits) overlaps Latin Priapea (0.1456 bits, p = 0.38).
Bowern & Lindemann (2020/2021) Whole-stream conditional character entropy h_2 = H_2 - H_1 = 2.11..2.19 bits without line-frame stripping or Hand x Language stratification. Core_Only(C) wrapper ablation + Davis Hand (1..5) x Currier (A/B) + section stratification across 165 folios (s60 c-506..c-507, s77 c-510, L-303, L-305). Whole-text h_2 combines line-edge wrapper predictability, cross-slot coupling, and scribe/section shifts (JSD = 0.0253, p = 0.0002). Within Herbal-B (s77), the Hand 2 vs Hand 3 grammar split disappears (explained by section/quire mixture).
Zattera (2022) + Stolfi (2000) Drops 20.7% non-consensus tokens (8,155 / 39,472) and fits an in-sample 12-slot regex without held-out quires or cross-slot MI accounting. Unfiltered ZL3b-n (34,475 P-tokens, 5,356 types), EXT-ZAT-2022 / s63 / s76 slot-entropy accounting (c-406..c-413, c-508..c-509), D-056 15-quire LOQO (s67, s69). Independent 12-slot product has entropy sum 16.71 vs word entropy 11.06 bits (5.65-bit over-generation; pairwise MI 1.4041 bits, z = +373.4). Held-out generalization requires cross-slot coupling + 1-step prefix memory (0.0434 bits).

Consequential corrections & scope limits

Where earlier internal claims were narrowed or withdrawn

What this lets us do & what it costs

Why the structural boundary matters and what would count as progress next

Having a falsifiable multi-scale benchmark lets us reject proposed decipherments and synthetic generators quickly when they match one global average (such as character entropy h_2 or Zipf's law) while failing line-reset framing, cross-slot coupling, or 15-quire LOQO transfer. The cost is that internal statistical triangulation cannot by itself bridge from production mechanics to semantic content.

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