The model asks what a record does as an administrative artifact before asking what its words sounded like.
ML-model-01 / Linear A
Reading function before language.
A structural-deductive lab for Linear A administrative documents: role assignment, invariant templates, and arithmetic validation without claiming phonetic decipherment.
Functional reconstruction No phonetic decipherment claim Constraint-first audit
Role labels such as AG, EN, QT, and OP are structural functions, not proposed phonetic values or lexical translations.
A plausible template is rejected or downgraded when totals, residuals, or damage policy do not support it.
Inventory register
KU-RO GRA 12
Template match
AG-EN-QT
Actor or office, commodity field, and quantity.
Arithmetic validation
total-type
12 = 12
Residual 0
Exact arithmetic validation: the recorded total equals the sum of item quantities.
Role system
ML-model-01 makes every token claim pass through a role test.
Agent
A sign-group behaving like an administrative actor, source, person, office, or responsible unit.
82 token types 179 lines
Commodity
A commodity-like field or item class connected to lists, logograms, and numeric accounting contexts.
95 token types 232 lines
Quantity
Numeral tokens assigned by membership first, then used as strict arithmetic evidence.
149 token types 312 lines
Operation
A balancing, summary, or operation context used to classify residuals and accounting behavior.
43 token types 74 lines
Unknown
A conservative bucket for near-threshold or structurally unstable tokens.
64 token types 85 lines
Audit cockpit
The brilliance of the model is that it knows when not to read.
Selected audit node 01
Corpus split
Claim
Dwork and Dsum are deliberately separated, so template inference and arithmetic evaluation do not contaminate each other.
Evidence
Dwork has 51 template-grade documents; Dsum has 68 summary-bearing documents, including 17 records not used for template extraction.
Risk controlled
If the same unstable documents trained and validated the model, the result would look stronger than it is.
Verdict Strong design choice
Template concentration
Three invariant templates account for most matched administrative lines.
Among 274 fully matched lines, the top three templates account for 204 lines, or 74.4% of matched structure in the reported working corpus.
Actor or office, commodity field, and quantity: the core administrative register shape.
Commodity, quantity, and operation marker: likely summary or balancing context.
Full administrative sequence: responsible unit, item, amount, and operation state.
Threshold stability
Raising thresholds reduces commitments and increases unknowns.
| theta | AG | EN | OP | UNK |
|---|---|---|---|---|
| 0.60 | 104 | 113 | 67 | 31 |
| 0.70 | 91 | 103 | 54 | 47 |
| 0.75 | 82 | 95 | 43 | 64 |
| 0.80 | 71 | 83 | 32 | 78 |
| 0.85 | 59 | 70 | 24 | 91 |
Dsum arithmetic outcomes
Classification is allowed to fail instead of forcing a reading.
Failure modes
A serious decipherment-adjacent system must publish its refusal rules.
Severe damage
More than 30% unreadable/uncertain token positions, damaged numeral field, or missing segmentation boundary.
Exclude from template inference; retain only low-confidence descriptive evidence where appropriate.Ambiguous segmentation
Multiple plausible line segmentations satisfy local tokenization rules.
Enumerate alternatives and classify only if one dominates by constraint satisfaction.Arithmetic inconsistency
Recorded total does not match item sums and no operator context licenses the residual.
Mark as unverifiable instead of forcing an administrative reading.Role instability
A token behaves differently across archives or falls near thresholds under sensitivity checks.
Keep as UNK or low-confidence; exclude from invariant template claims.Method pipeline
Constraint-first inference keeps the model from becoming speculative decipherment.
- Tokenize and normalize documents into line-level records.
- Compute positional bias and numeral adjacency in Dwork.
- Assign primary roles by thresholded constraints: QT, OP, AG, EN, UNK.
- Extract invariant role templates from stable non-UNK roles.
- Promote limited UNK tokens once using high-support slot evidence.
- Validate totals, residuals, and balance states on Dsum.
Uploaded source
ML-model-01: structural-deductive reconstruction of Minoan administration.
The public page presents the work as a functional reconstruction model: administrative roles, invariant templates, threshold sensitivity, ambiguity control, and arithmetic validation. It does not present phonetic decipherment as settled.
Open PDFGORILA, Younger transliterations, and sigLA sign control.
theta_AG 0.75, theta_OP 0.80, theta_EN 0.75, support minimum 10.
Dwork for templates; Dsum for arithmetic outcomes.
Functional reconstruction only; no lexical or phonetic claim is forced.