diff --git a/pyproject.toml b/pyproject.toml
index 6e63872..843d1e0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,6 +19,8 @@ dependencies = [
   "pandas>=3.0.2",
   "umap-learn>=0.5.12",
   "pyarrow>=24.0.0",
+  "pzflow>=4.0.0",
+  "shap>=0.51.0",
 ]
 
 [build-system]
diff --git a/scripts/aggregate/thresholded_metrics.py b/scripts/aggregate/thresholded_metrics.py
new file mode 100644
index 0000000..c1eb0e5
--- /dev/null
+++ b/scripts/aggregate/thresholded_metrics.py
@@ -0,0 +1,171 @@
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+import numpy as np
+from sklearn.covariance import OAS
+from sklearn.metrics import roc_auc_score
+
+ROOT = Path(__file__).resolve().parents[2] / "artifacts" / "route_comparison"
+DATASETS = ["cicids2017", "cicddos2019", "ciciot2023", "iscxtor2016"]
+SEEDS = (42, 43, 44)
+RNG_SPLIT = 12345
+
+
+def metrics_at_tau(d2_b, d2_a, tau):
+    tp = int((d2_a >= tau).sum())
+    fn = int((d2_a < tau).sum())
+    fp = int((d2_b >= tau).sum())
+    tn = int((d2_b < tau).sum())
+    prec = tp / max(tp + fp, 1)
+    rec = tp / max(tp + fn, 1)
+    f1 = 2 * prec * rec / max(prec + rec, 1e-9)
+    fpr = fp / max(fp + tn, 1)
+    return {"f1": f1, "prec": prec, "rec": rec, "fpr": fpr}
+
+
+def evaluate_seed(npz_path: Path) -> dict:
+    z = np.load(npz_path, allow_pickle=True)
+    keys = sorted(k.replace("val_", "") for k in z.files if k.startswith("val_") and not k.endswith("labels"))
+    val_S = np.stack([z[f"val_{k}"] for k in keys], axis=1)
+    atk_S = np.stack([z[f"atk_{k}"] for k in keys], axis=1)
+    val_S = np.nan_to_num(val_S, nan=0.0, posinf=1e6, neginf=-1e6)
+    atk_S = np.nan_to_num(atk_S, nan=0.0, posinf=1e6, neginf=-1e6)
+    K = val_S.shape[1]
+    rng = np.random.default_rng(RNG_SPLIT)
+    idx = rng.permutation(len(val_S))
+    half = len(idx) // 2
+    val_A = val_S[idx[:half]]
+    val_B = val_S[idx[half:]]
+    mu = val_A.mean(axis=0)
+    oas = OAS().fit(val_A)
+    inv_cov = np.linalg.inv(oas.covariance_ + 1e-9 * np.eye(K))
+
+    def d2(S):
+        d = S - mu
+        return np.einsum("ni,ij,nj->n", d, inv_cov, d)
+
+    d2_A = d2(val_A)
+    d2_B = d2(val_B)
+    d2_atk = d2(atk_S)
+    auroc = float(roc_auc_score(np.r_[np.zeros(len(d2_B)), np.ones(len(d2_atk))], np.r_[d2_B, d2_atk]))
+    out = {"AUROC": auroc, "n_val": len(val_S), "n_atk": len(atk_S)}
+    for pct, name in [(95, "P95"), (99, "P99")]:
+        tau = float(np.percentile(d2_A, pct))
+        m = metrics_at_tau(d2_B, d2_atk, tau)
+        out[f"F1@{name}"] = m["f1"]
+        out[f"Prec@{name}"] = m["prec"]
+        out[f"Recall@{name}"] = m["rec"]
+        out[f"FPR@{name}"] = m["fpr"]
+    return out
+
+
+def aggregate(dataset: str) -> dict:
+    rows = []
+    for s in SEEDS:
+        npz = ROOT / f"janus_{dataset}_seed{s}/phase1_scores.npz"
+        if not npz.exists():
+            print(f"[skip] {dataset} seed{s} — npz missing")
+            continue
+        rows.append(evaluate_seed(npz))
+    if not rows:
+        return {}
+    metrics = [k for k in rows[0] if k not in ("n_val", "n_atk")]
+    out = {"n_val": rows[0]["n_val"], "n_atk": rows[0]["n_atk"], "n_seeds": len(rows)}
+    for m in metrics:
+        a = np.array([r[m] for r in rows])
+        out[m] = {"mean": float(a.mean()), "std": float(a.std()), "per_seed": [float(x) for x in a]}
+    return out
+
+
+SUPERVISED_REF = {
+    "cicddos2019": {"method": "TIPSO-GAN (supervised, single seed)", "AUROC": 0.9999, "F1": 0.9989, "source": "baselines/TIPSO-GAN/ndss_tipso_artifact/artifacts/perf_summary_cicddos2019.json"},
+}
+
+
+def render_md(by_ds: dict) -> str:
+    lines = []
+    lines.append("# JANUS Thresholded Metrics — Mahalanobis-OAS, 3-seed mean ± std")
+    lines.append("")
+    lines.append("Computed post-hoc from `janus_<ds>_seed{42,43,44}/phase1_scores.npz` — no retraining.")
+    lines.append("")
+    lines.append("## Protocol")
+    lines.append("")
+    lines.append("- Aggregator: **Mahalanobis-OAS** distance over the 10-d JANUS raw score vector")
+    lines.append(f"- (μ, Σ) fit on **benign val half A** (random split seed={RNG_SPLIT}); F1/Precision/Recall/FPR measured on **benign val half B + ALL attacks**")
+    lines.append("- AUROC measured on (half B + attacks)")
+    lines.append("- Thresholds: τ95 = 95th percentile of d² on half A; τ99 = 99th percentile")
+    lines.append("")
+    lines.append("## Headline (4 datasets × 3 seeds)")
+    lines.append("")
+    lines.append("| Dataset | n_val | n_atk | AUROC | F1@P95 | Prec@P95 | Recall@P95 | FPR@P95 | F1@P99 | TPR@P99 |")
+    lines.append("|---|---|---|---|---|---|---|---|---|---|")
+    for ds in DATASETS:
+        if ds not in by_ds or not by_ds[ds]:
+            lines.append(f"| {ds} | — | — | — | — | — | — | — | — | — |")
+            continue
+        d = by_ds[ds]
+
+        def cell(k):
+            v = d[k]
+            return f"{v['mean']:.4f} ± {v['std']:.4f}"
+
+        lines.append(
+            f"| {ds} | {d['n_val']} | {d['n_atk']} | {cell('AUROC')} | "
+            f"{cell('F1@P95')} | {cell('Prec@P95')} | {cell('Recall@P95')} | {cell('FPR@P95')} | "
+            f"{cell('F1@P99')} | {cell('Recall@P99')} |"
+        )
+    lines.append("")
+    if any(ds in SUPERVISED_REF and ds in by_ds and by_ds[ds] for ds in DATASETS):
+        lines.append("## Supervised SOTA reference (cell-by-cell)")
+        lines.append("")
+        lines.append("Single-seed published numbers from supervised methods, where available, for context. The protocols are not directly comparable (supervised uses attack labels at training); this is meant to show the ceiling, not for head-to-head SOTA claim.")
+        lines.append("")
+        lines.append("| Dataset | Supervised method | Sup AUROC | Sup F1 | JANUS AUROC | JANUS F1@P95 | Δ AUROC | Δ F1 |")
+        lines.append("|---|---|---|---|---|---|---|---|")
+        for ds in DATASETS:
+            if ds not in SUPERVISED_REF or ds not in by_ds or not by_ds[ds]:
+                continue
+            ref = SUPERVISED_REF[ds]
+            d = by_ds[ds]
+            lines.append(
+                f"| {ds} | {ref['method']} | {ref['AUROC']:.4f} | {ref['F1']:.4f} | "
+                f"{d['AUROC']['mean']:.4f} ± {d['AUROC']['std']:.4f} | "
+                f"{d['F1@P95']['mean']:.4f} ± {d['F1@P95']['std']:.4f} | "
+                f"{d['AUROC']['mean'] - ref['AUROC']:+.4f} | "
+                f"{d['F1@P95']['mean'] - ref['F1']:+.4f} |"
+            )
+        lines.append("")
+    for ds in DATASETS:
+        if ds not in by_ds or not by_ds[ds]:
+            continue
+        d = by_ds[ds]
+        lines.append(f"## {ds}")
+        lines.append("")
+        lines.append(f"n_val={d['n_val']}, n_atk={d['n_atk']}, n_seeds={d['n_seeds']}")
+        lines.append("")
+        lines.append("| Metric | seed42 | seed43 | seed44 | mean ± std |")
+        lines.append("|---|---|---|---|---|")
+        for m in ["AUROC", "F1@P95", "Prec@P95", "Recall@P95", "FPR@P95", "F1@P99", "Prec@P99", "Recall@P99", "FPR@P99"]:
+            v = d[m]
+            ps = v["per_seed"]
+            lines.append(f"| {m} | {ps[0]:.4f} | {ps[1]:.4f} | {ps[2]:.4f} | {v['mean']:.4f} ± {v['std']:.4f} |")
+        lines.append("")
+    return "\n".join(lines) + "\n"
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument("--datasets", nargs="*", default=DATASETS)
+    p.add_argument("--out", type=Path, default=ROOT / "THRESHOLDED.md")
+    args = p.parse_args()
+    by_ds = {ds: aggregate(ds) for ds in args.datasets}
+    md = render_md(by_ds)
+    args.out.write_text(md)
+    print(md)
+    print(f"\n[wrote] {args.out}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/baselines/run_shafir_nf_csv.py b/scripts/baselines/run_shafir_nf_csv.py
index 3dfed2b..1141534 100644
--- a/scripts/baselines/run_shafir_nf_csv.py
+++ b/scripts/baselines/run_shafir_nf_csv.py
@@ -12,7 +12,7 @@ import pandas as pd
 os.environ.setdefault('JAX_PLATFORMS', 'cpu')
 warnings.filterwarnings('ignore')
 import optax
-from pzflow import Flow
+from pzflow import Flow, FlowEnsemble
 from sklearn.metrics import average_precision_score, roc_auc_score
 from sklearn.preprocessing import StandardScaler
 REPO = Path(__file__).resolve().parents[2]
@@ -175,7 +175,7 @@ def _safe_metric(fn, y, s) -> float:
     except ValueError:
         return float('nan')
 
-def _train_and_score(train, val, atk, feat_cols, *, epochs, lr, optimizer):
+def _train_and_score(train, val, atk, feat_cols, *, epochs, lr, optimizer, n_flows=1, seed=0):
     raw_train = train[feat_cols].astype(np.float64).values
     keep = raw_train.std(axis=0) > 0
     if not keep.all():
@@ -200,9 +200,16 @@ def _train_and_score(train, val, atk, feat_cols, *, epochs, lr, optimizer):
         opt = optax.sgd(learning_rate=lr)
     else:
         opt = optax.adam(learning_rate=lr)
-    flow = Flow(df_train.columns.tolist())
+    if n_flows > 1:
+        flow = FlowEnsemble(df_train.columns.tolist(), N=n_flows)
+    else:
+        flow = Flow(df_train.columns.tolist())
     t0 = time.time()
-    losses = flow.train(df_train, optimizer=opt, epochs=epochs, verbose=False)
+    if n_flows > 1:
+        losses_dict = flow.train(df_train, optimizer=opt, epochs=epochs, verbose=False, seed=seed)
+        losses = list(losses_dict.values())[0]
+    else:
+        losses = flow.train(df_train, optimizer=opt, epochs=epochs, verbose=False)
     t_train = time.time() - t0
     t0 = time.time()
     lp_val = np.asarray(flow.log_prob(df_val))
@@ -229,6 +236,7 @@ def main():
     p.add_argument('--epochs', type=int, default=100)
     p.add_argument('--lr', type=float, default=0.001)
     p.add_argument('--optimizer', choices=['sgd', 'adam'], default='sgd')
+    p.add_argument('--n-flows', type=int, default=1, help='1 = single NF (Shafir paper baseline mode); 2 = paper headline ensemble')
     args = p.parse_args()
     args.out_dir.mkdir(parents=True, exist_ok=True)
     (src_name, tgt_name, caps) = PROTOCOL_CONFIG[args.protocol]
@@ -247,15 +255,16 @@ def main():
         print(f'  [features] within: {len(feat_cols)} cols')
         (train, val, atk) = _sample_within(src_df, caps, args.seed)
     print(f'  [data] train={len(train):,}  val={len(val):,}  attack={len(atk):,}  D={len(feat_cols)}')
-    res = _train_and_score(train, val, atk, feat_cols, epochs=args.epochs, lr=args.lr, optimizer=args.optimizer)
+    res = _train_and_score(train, val, atk, feat_cols, epochs=args.epochs, lr=args.lr, optimizer=args.optimizer, n_flows=args.n_flows, seed=args.seed)
     (val_score, atk_score) = (res['score_val'], res['score_atk'])
     y = np.r_[np.zeros(len(val_score)), np.ones(len(atk_score))]
     s = np.r_[val_score, atk_score]
     overall = {'neg_log_prob': {'auroc': _safe_metric(roc_auc_score, y, s), 'auprc': _safe_metric(average_precision_score, y, s)}}
     a_labels = atk['cls_label'].astype(str).to_numpy()
     per_cls = _per_class(val_score, atk_score, a_labels)
-    out = {'method': 'shafir_nf_csv', 'protocol': args.protocol, 'seed': args.seed, 'src_dataset': src_name, 'tgt_dataset': tgt_name, 'feature_set': feat_cols, 'n_features': len(feat_cols), 'n_train': len(train), 'n_val': len(val), 'n_atk': len(atk), 'epochs': args.epochs, 'lr': args.lr, 'optimizer': args.optimizer, 't_train_sec': round(res['t_train'], 2), 't_score_sec': round(res['t_score'], 2), 'loss_first_last': [float(res['losses'][0]), float(res['losses'][-1])], 'overall': overall, 'per_class': per_cls}
-    out_json = args.out_dir / f'{args.protocol}_seed{args.seed}.json'
+    out = {'method': 'shafir_nf_csv', 'protocol': args.protocol, 'seed': args.seed, 'n_flows': args.n_flows, 'src_dataset': src_name, 'tgt_dataset': tgt_name, 'feature_set': feat_cols, 'n_features': len(feat_cols), 'n_train': len(train), 'n_val': len(val), 'n_atk': len(atk), 'epochs': args.epochs, 'lr': args.lr, 'optimizer': args.optimizer, 't_train_sec': round(res['t_train'], 2), 't_score_sec': round(res['t_score'], 2), 'loss_first_last': [float(res['losses'][0]), float(res['losses'][-1])], 'overall': overall, 'per_class': per_cls}
+    suffix = f"_n{args.n_flows}" if args.n_flows > 1 else ""
+    out_json = args.out_dir / f'{args.protocol}_seed{args.seed}{suffix}.json'
     out_json.write_text(json.dumps(out, indent=2))
     npz_path = out_json.with_suffix('.npz')
     np.savez_compressed(npz_path, b_neg_log_prob=val_score, a_neg_log_prob=atk_score, a_labels=a_labels.astype(str), losses=res['losses'])
diff --git a/uv.lock b/uv.lock
index 6b2075a..1c6f9b2 100644
--- a/uv.lock
+++ b/uv.lock
@@ -4,10 +4,25 @@ requires-python = ">=3.12"
 resolution-markers = [
     "python_full_version >= '3.14' and sys_platform == 'win32'",
     "python_full_version >= '3.14' and sys_platform == 'emscripten'",
-    "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
-    "python_full_version < '3.14' and sys_platform == 'win32'",
-    "python_full_version < '3.14' and sys_platform == 'emscripten'",
-    "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
+    "(python_full_version >= '3.14' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')",
+    "python_full_version == '3.13.*' and sys_platform == 'win32'",
+    "python_full_version < '3.13' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and sys_platform == 'emscripten'",
+    "python_full_version < '3.13' and sys_platform == 'emscripten'",
+    "python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
+    "python_full_version < '3.13' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
+    "(python_full_version == '3.13.*' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')",
+    "(python_full_version < '3.13' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version < '3.13' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')",
+]
+
+[[package]]
+name = "absl-py"
+version = "2.4.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/64/c7/8de93764ad66968d19329a7e0c147a2bb3c7054c554d4a119111b8f9440f/absl_py-2.4.0.tar.gz", hash = "sha256:8c6af82722b35cf71e0f4d1d47dcaebfff286e27110a99fc359349b247dfb5d4", size = 116543, upload-time = "2026-01-28T10:17:05.322Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/18/a6/907a406bb7d359e6a63f99c313846d9eec4f7e6f7437809e03aa00fa3074/absl_py-2.4.0-py3-none-any.whl", hash = "sha256:88476fd881ca8aab94ffa78b7b6c632a782ab3ba1cd19c9bd423abc4fb4cd28d", size = 135750, upload-time = "2026-01-28T10:17:04.19Z" },
 ]
 
 [[package]]
@@ -64,6 +79,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e4/20/71885d8b97d4f3dde17b1fdb92dbd4908b00541c5a3379787137285f602e/click-8.3.2-py3-none-any.whl", hash = "sha256:1924d2c27c5653561cd2cae4548d1406039cb79b858b747cfea24924bbc1616d", size = 108379, upload-time = "2026-04-03T19:14:43.505Z" },
 ]
 
+[[package]]
+name = "cloudpickle"
+version = "3.1.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/27/fb/576f067976d320f5f0114a8d9fa1215425441bb35627b1993e5afd8111e5/cloudpickle-3.1.2.tar.gz", hash = "sha256:7fda9eb655c9c230dab534f1983763de5835249750e85fbcef43aaa30a9a2414", size = 22330, upload-time = "2025-11-03T09:25:26.604Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/88/39/799be3f2f0f38cc727ee3b4f1445fe6d5e4133064ec2e4115069418a5bb6/cloudpickle-3.1.2-py3-none-any.whl", hash = "sha256:9acb47f6afd73f60dc1df93bb801b472f05ff42fa6c84167d25cb206be1fbf4a", size = 22228, upload-time = "2025-11-03T09:25:25.534Z" },
+]
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -144,7 +168,7 @@ name = "cuda-bindings"
 version = "12.9.6"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cuda-pathfinder", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
+    { name = "cuda-pathfinder", marker = "(platform_machine != 'x86_64' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/50/04/8a4d45dc154a8a32982658cc55be291e9778d1197834b15d33427e2f65c1/cuda_bindings-12.9.6-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0ea331bc47d9988cc61f0ecc5fa8df9dd188b4493ae1c6688bb1ee8ce8ba1af4", size = 7050347, upload-time = "2026-03-11T14:47:35.221Z" },
@@ -416,8 +440,10 @@ dependencies = [
     { name = "pandas" },
     { name = "pyarrow" },
     { name = "pyyaml" },
+    { name = "pzflow" },
     { name = "scapy" },
     { name = "scikit-learn" },
+    { name = "shap" },
     { name = "torch" },
     { name = "torchdiffeq" },
     { name = "torchvision" },
@@ -439,8 +465,10 @@ requires-dist = [
     { name = "pandas", specifier = ">=3.0.2" },
     { name = "pyarrow", specifier = ">=24.0.0" },
     { name = "pyyaml", specifier = ">=6.0" },
+    { name = "pzflow", specifier = ">=4.0.0" },
     { name = "scapy", specifier = ">=2.7.0" },
     { name = "scikit-learn", specifier = ">=1.8.0" },
+    { name = "shap", specifier = ">=0.51.0" },
     { name = "torch", specifier = ">=2.9.1", index = "https://download.pytorch.org/whl/cu128" },
     { name = "torchdiffeq", specifier = ">=0.2.5" },
     { name = "torchvision", specifier = ">=0.24.1", index = "https://download.pytorch.org/whl/cu128" },
@@ -450,6 +478,52 @@ requires-dist = [
 [package.metadata.requires-dev]
 dev = [{ name = "pytest", specifier = ">=9.0.3" }]
 
+[[package]]
+name = "jax"
+version = "0.10.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "jaxlib" },
+    { name = "ml-dtypes" },
+    { name = "numpy" },
+    { name = "opt-einsum" },
+    { name = "scipy" },
+]
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