teacher measured at greedy: PER 1.25% — same-protocol client-tier gap is +1.60pp

The beam pathology inflated the teacher too (4.43 beam-4 → 1.25
greedy, n=1219, EM 95.16%). The Thai client tier's true shrink cost is
+1.60pp greedy-to-greedy, not the +7.63pp the beam-vs-beam comparison
suggested. Frontier figure, RESULTS table, metadata, metrics chain all
updated.

Author: Ronald Tse

docs/RESULTS.md

@@ -85,10 +85,15 @@ better than the beam-4 harness numbers.** Re-measured on the shipped
 int4 zip through the Python runtime (the exact ONNX KV decode users
 get), true Levenshtein, full 1,219-sentence set:
 
-| Decode | PER | Exact match |
-|---|---|---|
-| beam-4 (published, torch harness) | 12.06% | 87.94% |
-| **greedy (runtime protocol)** | **2.85%** | **88.93%** |
+| Decode | Teacher PER | Student PER | Student EM |
+|---|---|---|---|
+| beam-4 (published, torch harness) | 4.43% | 12.06% | 87.94% |
+| **greedy (runtime protocol)** | **1.25%** | **2.85%** | **88.93%** |
+
+The teacher is also affected by the beam pathology (4.43 beam-4 → 1.25
+greedy, measured 2026-08-25 through the same harness at num_beams=1):
+same-protocol, the client tier's true shrink cost is **+1.60pp**, not
+the +7.63pp the beam-vs-beam comparison suggested.
 
 The beam-4 numbers are inflated by length-normalized beam preferring
 long garbage on this model's flat per-token distributions (top-1

docs/paper-assets/frontier.png

@@ -69,13 +69,14 @@ heb-diac-small-1.0:
     - {row: "Student (ByT5-small, gate)", column: DER, as: der_student_greedy}
 tha-g2p-small-1.0:
   repo: interscript/interscript-ml
-  ref: docs/greedy-correction
+  ref: docs/paper-ci
   path: docs/RESULTS.md
   anchor: tha-g2p-small-10-thai-g2p-client-tier-2026-08-22
   protocol: "greedy decode via the Python runtime (the shipped ONNX KV path), corpus-level PER, true Levenshtein; 1,219 held-out Kaikki Thai test sentences"
   tables:
-    - {row: "beam-4 (published, torch harness)", column: PER, as: per_student_beam4}
-    - {row: "greedy (runtime protocol)", column: PER, as: per_student}
+    - {row: "greedy (runtime protocol)", column: Teacher PER, as: per_teacher_greedy}
+    - {row: "beam-4 (published, torch harness)", column: Student PER, as: per_student_beam4}
+    - {row: "greedy (runtime protocol)", column: Student PER, as: per_student}
 ara-diac-small-1.0:
   repo: interscript/interscript-ml
   ref: release/ara-diac-small-1.0

models/metrics-sources.yaml

@@ -17,6 +17,12 @@ trained_from: 'sequence-level KD from the B-K/umt5-thai-g2p-v2-0.5k teacher (4.4
   this is the smallest rung that does not collapse (docs/RESULTS.md frontier table,
   ~300MB int8).'
 metrics:
+- name: per_teacher_greedy
+  value: 1.25
+  protocol: greedy decode (num_beams=1) through the same harness; 1,219 Kaikki Thai
+    test sentences; exact match 95.16%; measured 2026-08-25 — the published beam-4
+    teacher figure 4.43 was itself inflated by the decode pathology
+  source: interscript/interscript-ml docs/RESULTS.md#tha-g2p-small-1.0
 - name: per_student_beam4
   value: 12.06
   protocol: beam-4, corpus-level PER, same harness as the teacher (src/gpu/ modal_distill.py::evaluate_per,