docs(paper): the 2.0 client rung — 4.822 on the leaderboard and frontier

E4 compounded the two measured levers on the vanilla architecture (r7
labels + Muon): 8.259 → 4.822, a 42% error reduction at identical size.
Leaderboard carries both rungs; the factorial paragraph gains the E4
sentence; the measurement-lesson paragraph closes with the lever
attribution (optimizer −2.96pp, fresher labels −0.47pp).

Author: Ronald Tse

docs/paper.adoc

@@ -209,14 +209,15 @@ External claims are made only where a public benchmark exists and the full proto
 |Gemini Flash 2.0 (published) |— |3.1926
 |GPT-4 (published) |— |3.8645
 |Sadeed (published) |1.5B |7.2915
-|our client student (ByT5-small) |300M |8.259
+|**our client student 2.0 (ByT5-small)** |300M |**4.822**
+|our client student 1.0 (ByT5-small) |300M |8.259
 |===
 
 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 current rung (r7) adds a teacher-labeled news-domain mix to the morphological-auxiliary teacher (r6) and improves both surfaces at once: in-domain 2.5793 → 2.2864, and out-of-domain WikiNews-2024 multi-reference 19.82/12.46 → 17.38/11.83 (WER/DER) — the paragraph-specialization trade-off of the earlier lineage is gone.
 
 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.
+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. The measured levers have since moved it: the 2.0 rung (r7-teacher labels + the Muon optimizer, pre-registered as E4) reaches **4.822** — a 42% error reduction at identical architecture and artifact size — with the optimizer contributing −2.96pp and the teacher's fresher labels −0.47pp of the −3.44pp total; the factorial decomposition of the remaining gap is in <<section-frontier>>.
 
 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 scores 17.38 WER against QCRI's in-domain-trained 2.70 — a domain gap (classical hadith vs news text), recorded rather than averaged away.
 
@@ -265,7 +266,7 @@ A fourth question completes the frontier analysis: at the shipped rung, is the r
 |r6 teacher |— |580M |2.582
 |===
 
-The 2×2 closes cleanly: the optimizer alone recovers −2.96pp, memory alone −0.70pp (−0.47 under Muon), both together −3.43pp — roughly additive, slightly sub-additive on the memory term. The gap at this rung decomposes into ≈3.0pp optimization + ≈0.6pp capacity + ≈2.2pp residual (domain coverage; the full-benchmark subset lesson of <<section-leaderboard>> measures exactly this exposure). Two practical notes travel with the numbers: Muon's training CE was ~3× lower at equal steps *and* ~2.8× faster per step on this workload (1450-byte windows dominate; Newton–Schulz is cheap next to them), and the optimizer adopt gate (≥0.3pp, set before training) was exceeded nine-fold — while the capacity arm, measured under the same discipline, stayed below its 1.0pp bar. Optimization — not parameter count — is the dominant recoverable term of the distillation gap at this rung. (This speaks to the *distillation* gap on a pretrained backbone; it does not reopen the from-scratch collapse of finding 1, which is an initialization effect.)
+The 2×2 closes cleanly: the optimizer alone recovers −2.96pp, memory alone −0.70pp (−0.47 under Muon), both together −3.43pp — roughly additive, slightly sub-additive on the memory term. The gap at this rung decomposes into ≈3.0pp optimization + ≈0.6pp capacity + ≈2.2pp residual (domain coverage; the full-benchmark subset lesson of <<section-leaderboard>> measures exactly this exposure). Two practical notes travel with the numbers: Muon's training CE was ~3× lower at equal steps *and* ~2.8× faster per step on this workload (1450-byte windows dominate; Newton–Schulz is cheap next to them), and the optimizer adopt gate (≥0.3pp, set before training) was exceeded nine-fold — while the capacity arm, measured under the same discipline, stayed below its 1.0pp bar. Optimization — not parameter count — is the dominant recoverable term of the distillation gap at this rung. A final pre-registered arm (E4) compounded the two positive levers on the vanilla architecture — the r7 canonical teacher's labels plus Muon — and landed at 4.822, matching the PKM arm (4.829) without the memory layers; that rung ships as ara-diac-small-2.0. (This speaks to the *distillation* gap on a pretrained backbone; it does not reopen the from-scratch collapse of finding 1, which is an initialization effect.)
 
 == The decode protocol is part of the measurement
 [[section-decode]]