docs(paper): r8 controlled ablation — morphological aux beats phonemic

Identical run differing only in the aux stream's output representation
(broad-phonemic IPA vs morphology): 2.6588 vs 2.5793 vs 2.6775 baseline,
with the IPA projection itself learned (2.3% CER probe). The r6 win is
lexical-morphological knowledge injection, not phonemic supervision.

Author: Ronald Tse

docs/paper.adoc

@@ -212,6 +212,8 @@ External claims are made only where a public benchmark exists and the full proto
 
 The teacher is the best dedicated (task-trained, runnable-locally) model measured under this protocol — second only to a frontier proprietary LLM's published number, ahead of a clean GLM-5.2 reproduction, Gemini Flash, and GPT-4 — and it does so at 580M parameters against Sadeed's 1.5B.
 
+The r6 teacher's auxiliary-task design is validated by a controlled ablation: an identical run differing only in the auxiliary stream's output representation — broad-phonemic IPA of the same training units (deterministic converter) in place of morphological analysis — scores 2.6588 (vs 2.6775 for no auxiliary task and 2.5793 for the morphological one), while a held-out probe confirms the phonemic projection itself was learned (2.3% CER on the IPA stream). The improvement is therefore attributable to lexical-morphological knowledge injection, not to phonemic supervision per se: the strong form of the "phonemes help diacritization" hypothesis fails where its weak form (any structured auxiliary projection beats none) barely holds.
+
 The client student's row carries a measurement lesson we record rather than bury. Its first-published figure, 3.66, was measured on the first 300 paragraphs of the benchmark; the full 1,200-paragraph run scores 8.26 (teacher reproduces at 2.5815 against its documented 2.5793, confirming protocol consistency). The subset was not representative: its paragraphs sit closer to the student's training domain, and the remaining 900 expose a domain-generalization gap the subset hid. Two practices follow, now standing rules: student-tier numbers are published only from full benchmark sets, and a subset figure is labeled as such at first publication. The student ships with the full-set number disclosed — behind Sadeed-1.5B on the leaderboard — and closing its domain gap (more diverse label coverage, or on-policy distillation; <<section-discussion>>) is open work.
 
 Two protocol notes the benchmark's own authors make and we adopt: self-reported numbers from other protocols (Sadeed's repository reports 1.2% DER under its own split) are not comparable, and multi-reference evaluation (Mohamed & Mubarak 2025) is the honest treatment of valid-alternative diacritizations; on WikiNews-2024 our teacher family scores 19.82 WER against QCRI's in-domain-trained 2.70 — a domain gap (classical hadith vs news text), recorded rather than averaged away.