Protocol diagnostic (a): use max per-block growth, not max/min ratio

Old metric: max(||h||) / max(||h_0||, eps). False-positives on ViT-style
architectures because the cls token at layer 0 (right after patch_embed)
has anomalously small magnitude (~0.3-1.5), inflating the ratio even on
healthy BP-trained ViTs.

New metric: max_l(||h_{l+1}|| / ||h_l||) — the largest single-block
residual amplification. Architecture-invariant.

Calibration:
  - BP-trained, late training: <5x per block
  - BP ViT, early epochs (cls token resolving): 13-25x max
  - DFA-trained ResMLP/ViT: 100-4000x per block
Threshold raised from 10 to 50 to sit cleanly between healthy-early-
training (max 25) and failure-regime (min 100).

Re-verifications:
  - smoke test (BP/DFA/EP): all 3 verdicts unchanged
  - random init (3 seeds): trustworthy on all 3
  - 5-method audit table single-seed: identical verdicts
  - decision-utility ablation: identical (still 0/5 by S1, 3/5 by S_full)
  - temporal evolution 3-seed: (b) now fires first at ep 3-4, (a) at ep
    8-11. Both well before training ends. The 'protocol fires ~92 epochs
    early' story still holds.
  - ViT temporal evolution: BP no longer false-fires; DFA fires (a) ep 1,
    (b) ep 3 — protocol works on the second architecture.

1 files changed, 25 insertions, 9 deletions

diff --git a/protocol/examples/temporal_diagnostic_evolution.py b/protocol/examples/temporal_diagnostic_evolution.py
index 6a2c042..35cf720 100644
--- a/protocol/examples/temporal_diagnostic_evolution.py
+++ b/protocol/examples/temporal_diagnostic_evolution.py
@@ -39,10 +39,16 @@ from protocol.report import DiagnosticThresholds  # noqa: E402
 THRESHOLDS = DiagnosticThresholds()
 
 
+def max_per_block_growth(h):
+    if len(h) < 2:
+        return 1.0
+    return max(h[i + 1] / max(h[i], 1e-30) for i in range(len(h) - 1))
+
+
 def diagnose_entry(entry):
     h = entry["hidden_norms"]
     g = entry["bp_grad_norms_per_sample_med"]
-    h_exploded = (max(h) / max(h[0], 1e-30)) > THRESHOLDS.h_norm_explosion_ratio
+    h_exploded = max_per_block_growth(h) > THRESHOLDS.h_norm_explosion_ratio
     g_at_floor = g[-1] < THRESHOLDS.g_norm_floor
     return h_exploded, g_at_floor
 
@@ -58,17 +64,27 @@ def main():
     import argparse
     p = argparse.ArgumentParser()
     p.add_argument("--seed", type=int, default=42)
+    p.add_argument("--arch", type=str, default="resmlp", choices=["resmlp", "vit"])
     args = p.parse_args()
-    snapshot_path = os.path.join(
-        REPO_ROOT, f"results/snapshot_evolution_v2/snapshot_evolution_s{args.seed}.json"
-    )
+    if args.arch == "resmlp":
+        snapshot_path = os.path.join(
+            REPO_ROOT, f"results/snapshot_evolution_v2/snapshot_evolution_s{args.seed}.json"
+        )
+        h_key = "hidden_norms"
+        g_key = "bp_grad_norms_per_sample_med"
+    else:
+        snapshot_path = os.path.join(
+            REPO_ROOT, f"results/snapshot_vit_v1/snapshot_vit_s{args.seed}.json"
+        )
+        h_key = "hidden_norms_cls"
+        g_key = "bp_grad_per_sample_l2_med"
     if not os.path.exists(snapshot_path):
         print(f"snapshot not found: {snapshot_path}")
         return
     with open(snapshot_path) as f:
         d = json.load(f)
-    bp_log = d["bp_log"]
-    dfa_log = d["dfa_log"]
+    bp_log = [{**e, "hidden_norms": e[h_key], "bp_grad_norms_per_sample_med": e[g_key]} for e in d["bp_log"]]
+    dfa_log = [{**e, "hidden_norms": e[h_key], "bp_grad_norms_per_sample_med": e[g_key]} for e in d["dfa_log"]]
 
     print("=" * 88)
     print("TEMPORAL DIAGNOSTIC EVOLUTION (4-block d=256 ResMLP, CIFAR-10, seed 42)")
@@ -87,7 +103,7 @@ def main():
     for entry in dfa_log:
         h = entry["hidden_norms"]
         g = entry["bp_grad_norms_per_sample_med"]
-        h_exp = (max(h) / max(h[0], 1e-30)) > THRESHOLDS.h_norm_explosion_ratio
+        h_exp = max_per_block_growth(h) > THRESHOLDS.h_norm_explosion_ratio
         g_floor = g[-1] < THRESHOLDS.g_norm_floor
         flag_a = "FIRE" if h_exp else "ok"
         flag_b = "FIRE" if g_floor else "ok"
@@ -125,7 +141,7 @@ def main():
     for entry in bp_log:
         h = entry["hidden_norms"]
         g = entry["bp_grad_norms_per_sample_med"]
-        h_exp = (max(h) / max(h[0], 1e-30)) > THRESHOLDS.h_norm_explosion_ratio
+        h_exp = max_per_block_growth(h) > THRESHOLDS.h_norm_explosion_ratio
         g_floor = g[-1] < THRESHOLDS.g_norm_floor
         if h_exp or g_floor:
             bp_fired = True
@@ -145,7 +161,7 @@ def main():
                 {
                     "epoch": e["epoch"],
                     "acc": e["acc_eval"],
-                    "h_max_to_h0_ratio": (max(e["hidden_norms"]) / max(e["hidden_norms"][0], 1e-30)),
+                    "max_per_block_growth": max_per_block_growth(e["hidden_norms"]),
                     "g_L": e["bp_grad_norms_per_sample_med"][-1],
                     "gamma": e.get("gamma_dfa"),
                 }