Add audit table example: protocol applied to BP/DFA/SB/CB/EP

5-method audit table on 4-block d=256 ResMLP CIFAR-10 seed 42:
  - BP: trustworthy (acc 0.615, h_L=2e2, g_L=4e-4, stab 0.099)
  - DFA: walked back via (a)+(b)+(d) — h_L=4e8, g_L=4e-9, undercuts frozen
  - State Bridge: walked back via all 4 diagnostics — stability 0.992 is the
    cleanest possible drift-dominated case
  - Credit Bridge: walked back via all 4 — stability 0.352, also drift mode
  - EP: trustworthy (acc 0.359, h_L=3e3, g_L=2e-4, stab -0.036) — paper's
    internal control case

This is the §2 audit evidence for the main-track paper. Confirms that
standard headline acc + Γ silently fails on 3 of 5 methods on this
architecture, while the 4-diagnostic protocol catches all three.

2 files changed, 162 insertions, 0 deletions

diff --git a/protocol/examples/__init__.py b/protocol/examples/__init__.py
new file mode 100644
index 0000000..e69de29
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+++ b/protocol/examples/__init__.py
diff --git a/protocol/examples/audit_table.py b/protocol/examples/audit_table.py
new file mode 100644
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+++ b/protocol/examples/audit_table.py
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+"""
+Reproduce the §2 audit table: apply the diagnostic protocol to BP / DFA /
+State Bridge / Credit Bridge / EP checkpoints on the 4-block d=256 ResMLP /
+CIFAR-10 setup. Single seed 42 for the table; the paper uses 3-seed means
+elsewhere.
+
+Output is a per-method tabular summary that lists, for each diagnostic,
+the per-layer values and the verdict. This is the audit evidence behind the
+paper claim *"standard FA evaluation reports headline accuracy + Γ as
+evidence of training, but on modern pre-LN residual networks both signals
+silently fail for non-BP methods."*
+
+Run:
+    CUDA_VISIBLE_DEVICES=2 python -m protocol.examples.audit_table
+"""
+import os
+import sys
+import json
+
+import torch
+import torchvision
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader
+
+REPO_ROOT = os.path.dirname(
+    os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+)
+sys.path.insert(0, REPO_ROOT)
+
+from models.residual_mlp import ResidualMLP  # noqa: E402
+from protocol import diagnose  # noqa: E402
+
+CHECKPOINT_DIR = os.path.join(REPO_ROOT, "results/confirmatory/checkpoints_A2")
+EP_CHECKPOINT_DIR = os.path.join(REPO_ROOT, "results/ep_baseline")
+OUT_DIR = os.path.join(REPO_ROOT, "results/protocol_audit")
+os.makedirs(OUT_DIR, exist_ok=True)
+
+
+def load_eval_batches(n_batches=10, batch_size=128, device="cuda:0"):
+    tv = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616)),
+    ])
+    te = torchvision.datasets.CIFAR10("./data", train=False, download=True, transform=tv)
+    loader = DataLoader(te, batch_size=batch_size, shuffle=False, num_workers=0)
+    batches = []
+    for x, y in loader:
+        x = x.view(x.size(0), -1).to(device)
+        y = y.to(device)
+        batches.append((x, y))
+        if len(batches) >= n_batches:
+            break
+    return batches
+
+
+def evaluate(model, device):
+    tv = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616)),
+    ])
+    te = torchvision.datasets.CIFAR10("./data", train=False, download=True, transform=tv)
+    loader = DataLoader(te, batch_size=256, shuffle=False, num_workers=0)
+    model.eval()
+    correct = total = 0
+    with torch.no_grad():
+        for x, y in loader:
+            x = x.view(x.size(0), -1).to(device)
+            y = y.to(device)
+            preds = model(x).argmax(-1)
+            correct += (preds == y).sum().item()
+            total += x.size(0)
+    return correct / total
+
+
+def load_model(method: str, seed: int, device):
+    if method == "ep":
+        path = os.path.join(EP_CHECKPOINT_DIR, f"ep_s{seed}.pt")
+    else:
+        path = os.path.join(CHECKPOINT_DIR, f"{method}_s{seed}.pt")
+    ckpt = torch.load(path, map_location=device, weights_only=False)
+    sd = ckpt if not hasattr(ckpt, "state_dict") else ckpt.state_dict()
+    if isinstance(sd, dict) and "state_dict" in sd:
+        sd = sd["state_dict"]
+    model = ResidualMLP(input_dim=3072, d_hidden=256, num_classes=10, num_blocks=4).to(device)
+    model.load_state_dict(sd)
+    return model
+
+
+# 3-seed mean shallow / frozen baseline accuracies (from
+# project_resmlp_walkback_dfa_destroys_value memory entry — these are the
+# same number for the DFA condition by design: the "deep blocks frozen at
+# random init" is informationally equivalent to "no deep blocks").
+FROZEN_BASELINE_ACC = {
+    "bp": None,                  # BP-frozen is 34.6%; not the right comparator for BP-trainable
+    "dfa": 0.349,                # DFA-frozen / DFA-shallow 3-seed mean
+    "state_bridge": 0.349,       # uses the same architecture-matched control
+    "credit_bridge": 0.349,
+    "ep": None,                  # EP frozen-control not run yet
+}
+
+
+def main():
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    print(f"Device: {device}")
+    eval_batches = load_eval_batches(n_batches=10, batch_size=128, device=device)
+
+    methods = ["bp", "dfa", "state_bridge", "credit_bridge", "ep"]
+    rows = []
+    reports = {}
+    for method in methods:
+        print(f"\n### {method.upper()} (seed 42)")
+        model = load_model(method, 42, device)
+        acc = evaluate(model, device)
+        report = diagnose(
+            model=model,
+            eval_batches=eval_batches,
+            headline_acc=acc,
+            frozen_baseline_acc=FROZEN_BASELINE_ACC.get(method),
+            method_name=method.upper(),
+            notes="4-block d=256 ResMLP, CIFAR-10, seed 42",
+        )
+        print(report)
+        reports[method] = report.to_dict()
+        rows.append({
+            "method": method,
+            "acc": acc,
+            "h_L": report.residual_norms[-1],
+            "g_L": report.bp_grad_norms[-1],
+            "stability": report.cross_batch_stability,
+            "frozen_acc": report.frozen_baseline_acc,
+            "verdict": report.verdict,
+        })
+
+    # Compact summary table
+    print("\n\n" + "=" * 100)
+    print("AUDIT SUMMARY (single seed 42, 4-block d=256 ResMLP, CIFAR-10)")
+    print("=" * 100)
+    header = (
+        f"{'method':<16}{'acc':>8}{'||h_L||':>14}{'||g_L||':>14}"
+        f"{'stab(L/2)':>12}{'frozen':>10}  verdict"
+    )
+    print(header)
+    print("-" * 100)
+    for r in rows:
+        frozen = "n/a" if r["frozen_acc"] is None else f"{r['frozen_acc']:.4f}"
+        print(
+            f"{r['method']:<16}"
+            f"{r['acc']:>8.4f}"
+            f"{r['h_L']:>14.3e}"
+            f"{r['g_L']:>14.3e}"
+            f"{r['stability']:>12.3f}"
+            f"{frozen:>10}  {r['verdict']}"
+        )
+
+    out_path = os.path.join(OUT_DIR, "audit_table_s42.json")
+    with open(out_path, "w") as f:
+        json.dump({"reports": reports, "summary": rows}, f, indent=2)
+    print(f"\nSaved {out_path}")
+
+
+if __name__ == "__main__":
+    main()