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+# Learning the Underdetermined: A Dispatch Doctrine for Neural Completion of Rule-Definable Script Mappings
+
+**Draft 1 — WO77/E2 (resources & evaluation paper). Owner review before any submission.**
+
+## Abstract
+
+Authority romanization systems are rules; the languages they romanize
+are not always rule-complete. Abjads omit vowels, abugidas imply them,
+and the residual ambiguity must be resolved by a model — a component
+that authorities do not govern and that the field has no shared
+methodology for building. This paper contributes a dispatch doctrine:
+an empirically derived mapping from (script class, ambiguity type,
+data regime) to training technique, grounded in a negative-results
+registry from a systematic campaign, two structural rules that govern
+the composition of learned components with rule systems, and an
+ambiguity-aware evaluation metric (valid-reading rate) that corrects
+the systematic undercounting of single-reference diacritization
+error rates. We report per-language results under external-protocol
+evaluation for Arabic, Urdu, Persian, Thai, Khmer, and Hebrew, and we
+release every verdict — positive and negative — in a machine-checkable
+registry.
+
+## 1. The underdetermination spectrum
+
+Script conversion mappings form a spectrum. At one pole, the mapping
+is a function the authority fully decrees: Cyrillic → Latin with a
+published correspondence table. At the other, the source orthography
+genuinely underdetermines the target: an Arabic skeleton (كتب) is a
+*relation* over readings (kataba, kutiba, kutub, ...), a Thai
+orthographic syllable underdetermines its tone, a Khmer cluster
+underdetermines its vowel. The intermediate zone — where the authority
+decrees conventions *conditioned on an inferred layer* — is where
+learned components belong.
+
+The design error this paper argues against is treating the whole
+spectrum as one problem class: applying one neural recipe to every
+language, or conversely, hand-rolling a new pipeline per language. The
+dispatch doctrine makes the technique choice a function of measurable
+properties of the language pair.
+
+## 2. The doctrine
+
+**Rule 1 — Vocalization is composition, not a sibling task.** An
+unvocalized skeleton is a relation over readings; vocalization upgrades
+it to a function, which the rule system then applies. The two must not
+be conflated: romanizing a bare skeleton by convention (what
+bibliographic systems do) is a different product from predicting the
+hidden reading. Composition, not pipeline ad-hocery: the rule language
+declares the call, the model fills the relation, the decode policy
+selects among licensed readings.
+
+**Rule 2 — Abugidas presuppose a reading.** Thai and Khmer
+orthographies are positional syllabaries: vowel signs interleave with
+consonant clusters, inherent vowels are implied, tone is computed from
+orthographic class — and the major romanization convention (RTGS)
+discards the tone that the orthography computes. The rule system is
+deterministic *over an inferred layer*. The doctrine: lexicon-first
+(dictionary hit-rate is the first-class metric), trained fallback for
+novel tokens, and a rule composite for the convention; the three
+target layers (toneless romanization, phonemic, phonemic + tone) are
+tracked separately.
+
+**The dispatch table** (script class × ambiguity × data regime →
+technique), with the registry-defaulted form: lexical ambiguity
+dominant → lexicon + morphology-conditioned SFT; phonotactic
+reordering → byte-level seq2seq (ByT5-class); register variance →
+register-stratified data; teacher availability → oracle relabel only
+when teacher arms disagree; distillation only across decorrelated
+teachers.
+
+## 3. The negative-results registry
+
+Four refuted techniques are recorded with the same rigor as positives,
+each with its motivating failure:
+
+- **Reinforcement learning for diacritization** is knowledge-limited at
+  SFT convergence: the reward signal cannot exceed what the supervised
+  model already knows.
+- **CTC** cannot model Thai phonotactics: the conditional independence
+  of CTC paths escapes the mode collapse of seq2seq training but
+  plateaus far above the byte-level seq2seq baseline.
+- **LLMs as label teachers** hallucinate diacritics at rates
+  incompatible with training; banned by registry law, enforced by the
+  pipeline validators.
+- **Distillation on correlated teacher arms** transfers nothing: arms
+  trained on the same data family agree on the same errors, leaving no
+  oracle signal. Complementarity, not accuracy, predicts transfer.
+
+## 4. Per-language programs
+
+| language | task | technique (dispatch) | result | external protocol |
+|---|---|---|---|---|
+| Arabic | diacritization | ByT5 SFT + oracle relabel + noisy-student chain | DER 2.29 (canonical), 4.57 (fast tier) | SadeedDiac-25 |
+| Arabic | news register | register-closure specialist | WER 8.85 | WikiNews-2024 multiref |
+| Urdu | diacritization | base-model SFT | CER 3.57 | held-out docs split |
+| Persian | diacritization | SFT + register work | CER 0.603 row-mean | 9,288-row docs split |
+| Thai | G2P | lexicon-first + ByT5 fallback (CTC refuted) | PER 2.85 | published G2P suite |
+| Khmer | transliteration | lexicon-conditioned ByT5 (in progress) | CER 27.4 → target < 15 | held-out corpus |
+| Hebrew | nikud | plane architecture (data-gated) | DER 8.18 int8 | ReNikud protocol |
+
+## 5. Evaluation: valid-reading rate
+
+Single-reference DER counts every divergence from one gold reading as
+an error, systematically undercounting models that produce *other
+licensed readings*. We propose reporting valid-reading rate (VRR) —
+the proportion of predictions that are dictionary-licensed readings —
+alongside DER/WER, and evaluating multireference sets with
+oracle-validity rather than best-match. The multireference
+reconciliation failures that motivated this are documented with
+examples.
+
+## 6. Governance
+
+Every claim in Section 4 links to a frozen artifact: a pinned corpus
+(SHA-256, license-reviewed), a conformance-vector set reproduced
+byte-exactly in three independent runtimes, a prediction file, and an
+external protocol. Verdicts live in a machine-checkable registry that
+the training pipeline consults; refuted techniques require a new
+register entry to retry.
+
+## 7. Conclusion
+
+The dispatch doctrine converts per-language modeling from craft to
+policy: measurable properties select the technique, the registry
+prevents re-treading refuted ground, and the evaluation admits
+legitimate ambiguity. The doctrine is released as registry defaults so
+that the next language is a configuration, not a research project.
+
+## References (to complete)
+
+- ByT5; sequitur G2P; SadeedDiac-25; ReNikud; PyThaiNLP.
+- The negative-results registry (interscript-models TODO.sota).
+- RTGS; UNGEGN romanization; the ISC formalism (companion paper).