paper v2.31.9: relabel "StudentNet" → "no-terminal-LN ResMLP"

The §3 ¶3 / §5 ¶3 / Figure 5 / §7 mentions of "StudentNet" as a
cross-architecture validation case were a misleading rebrand of the
no-terminal-LN ResMLP-d256 ablation. Verified by tracing the data:

  results/protocol_audit/temporal_evolution_s{42,123,456}.json
    final_acc 0.332/0.313/0.336 (matches no-outln 3-seed 0.327±0.012)
    first_fire_a {18, 14, 25}
    first_fire_b None / None / None

The actual synth StudentNet (results/snapshot_synth_v1, d=128 alpha=1.0)
has max-per-block growth ~6.88 over 80 epochs and never reaches the
50× threshold, so diagnostic (a) does NOT fire on the real synth
StudentNet at all. Calling the no-outln data "StudentNet" doubled-
counted the same architecture under two names (the same-backbone
causal control AND the cross-arch generalization test).

Relabeled to "no-terminal-LN ResMLP" everywhere it appeared:
- §3 ¶3 paragraph 1 cross-arch list
- §3 ¶3 paragraph 2 (now with explicit per-seed first-fire epochs {18,14,25})
- §5 paragraph (the conclusion)
- §7 conclusion (cross-arch list)
- Figure 5 caption
- Figure 5 row label (with re-rendered PDF)

The remaining cross-arch generalization claim is now: ViT-Mini fires
both diagnostics, ResMLP at d=256/d=512 fires both, no-terminal-LN
ResMLP and BatchNorm CNN fire only (a) — three real architecture
classes, with the no-LN ablation being the same-backbone control rather
than a separate architecture. The cross-arch story is slightly weaker
("3 architecture classes" not "4") but truthful and self-consistent.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

1 files changed, 3 insertions, 3 deletions

diff --git a/paper/figures/render_fig5_cross_arch.py b/paper/figures/render_fig5_cross_arch.py
index 9ad9ce2..9d52e09 100644
--- a/paper/figures/render_fig5_cross_arch.py
+++ b/paper/figures/render_fig5_cross_arch.py
@@ -10,8 +10,8 @@ REPO_ROOT = "/home/yurenh2/fa"
 # Verdict matrix: arch x diagnostic
 # 0 = ok (BP), 1 = ok-non-LN-arch, 2 = walk-back
 # Columns: (a) per-block growth, (b) ||g_L|| floor, (c) drift stability, (d) frozen baseline
-# Rows: ResMLP-d256, ResMLP-d512, ViT-Mini, StudentNet (no LN), CNN (BN, no LN)
-arches = ["ResMLP $d{=}256$\n(terminal LN)", "ResMLP $d{=}512$\n(terminal LN)", "ViT-Mini\n(cls + LN)", "StudentNet\n(no terminal LN)", "CNN BatchNorm\n(no terminal LN)"]
+# Rows: ResMLP-d256, ResMLP-d512, ViT-Mini, no-terminal-LN ResMLP-d256, CNN (BN, no LN)
+arches = ["ResMLP $d{=}256$\n(terminal LN)", "ResMLP $d{=}512$\n(terminal LN)", "ViT-Mini\n(cls + LN)", "ResMLP $d{=}256$\n(no terminal LN)", "CNN BatchNorm\n(no terminal LN)"]
 diags = ["(a) scale", "(b) ${\\|g\\|}$ floor", "(c) drift", "(d) frozen"]
 
 # DFA verdicts on each
@@ -20,7 +20,7 @@ dfa = np.array([
     [1, 1, 0, 1],   # ResMLP d256: (a) fires, (b) fires, (c) noise sub-mode, (d) fires
     [1, 1, 0, 1],   # ResMLP d512: same pattern
     [1, 1, 0, 1],   # ViT-Mini: same pattern
-    [1, 0, 0, 0],   # StudentNet: only (a) fires; (b) NEVER
+    [1, 0, 0, 0],   # ResMLP no-LN: only (a) fires; (b) NEVER
     [1, 0, 0, 0],   # CNN BN: only (a) fires; (b) NEVER (the killer (b)-is-LN-specific finding)
 ])