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diff --git a/analysis_2x2/analyze_clv.py b/analysis_2x2/analyze_clv.py
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+++ b/analysis_2x2/analyze_clv.py
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+"""Decisive test for (b): does the leading-CLV GEOMETRY predict correctness beyond
+drift + q_halt (which already fully explain the scalar spectrum)?
+
+For each geometric feature (leading-CLV H-fraction, token participation ratio, answer-token PR,
+readout alignment): raw AUC, partial corr with correctness | (drift+q_halt), and the multivariate
+held-out ridge-AUC GAIN from adding all CLV features on top of drift+q_halt.
+Gain ~ 0  => geometry also reducible (honest pivot to (a)).
+Gain > 0  => a non-reducible dynamical-geometry signal (chase further).
+"""
+from __future__ import annotations
+import sys, glob
+import numpy as np
+rng = np.random.default_rng(0)
+
+
+def resid(y, X):
+    A = np.concatenate([X, np.ones((len(y), 1))], 1)
+    b, _, _, _ = np.linalg.lstsq(A, y, rcond=None)
+    return y - A @ b
+
+
+def pcorr(a, b, Z):
+    return float(np.corrcoef(resid(a.astype(float), Z), resid(b.astype(float), Z))[0, 1])
+
+
+def auc(score, y):
+    pos, neg = score[y == 1], score[y == 0]
+    if len(pos) == 0 or len(neg) == 0:
+        return float("nan")
+    a = np.concatenate([pos, neg]); o = np.argsort(a); r = np.empty(len(a)); r[o] = np.arange(1, len(a) + 1)
+    return float((r[:len(pos)].sum() - len(pos) * (len(pos) + 1) / 2) / (len(pos) * len(neg)))
+
+
+def ridge_cv_auc(X, y, folds=5, lam=1.0):
+    n = len(y); idx = rng.permutation(n); Xs = (X - X.mean(0)) / (X.std(0) + 1e-8); aucs = []
+    for f in range(folds):
+        te = idx[f::folds]; tr = np.setdiff1d(idx, te)
+        A = np.concatenate([Xs[tr], np.ones((len(tr), 1))], 1)
+        w = np.linalg.solve(A.T @ A + lam * np.eye(A.shape[1]), A.T @ y[tr])
+        pr = np.concatenate([Xs[te], np.ones((len(te), 1))], 1) @ w
+        aucs.append(auc(pr, y[te]))
+    return float(np.nanmean(aucs))
+
+
+def go(tag, npz):
+    d = np.load(npz)
+    y = d["exact_correct"].astype(float)
+    dr = np.concatenate([d["drift_zH"], d["drift_zL"]], 1).astype(float)
+    qh = np.concatenate([d["q_halt"], d["q_continue"]], 1).astype(float)
+    ctrl = np.concatenate([dr, qh], 1)
+    feats = {nm: d[nm][:, 0].astype(float) for nm in ["clv_hfrac", "clv_pr", "clv_pr_ans", "clv_readout"]}
+    clv_all = np.concatenate([d[nm].astype(float) for nm in ["clv_hfrac", "clv_pr", "clv_pr_ans", "clv_readout"]], 1)
+    print(f"\n=== {tag}  (n={len(y)}, acc={y.mean():.3f}) ===")
+    print(f"{'feature':14s} {'rawAUC':>7} {'partial|drift+qhalt':>20}")
+    for nm, f in feats.items():
+        print(f"{nm:14s} {auc(f, y):>7.3f} {pcorr(f, y, ctrl):>20.3f}")
+    base = ridge_cv_auc(ctrl, y)
+    plus = ridge_cv_auc(np.concatenate([ctrl, clv_all], 1), y)
+    clv_alone = ridge_cv_auc(clv_all, y)
+    print(f"held-out ridge AUC: CLV-alone={clv_alone:.3f} | drift+qhalt={base:.3f} | +CLV-geom={plus:.3f}  GAIN={plus-base:+.3f}")
+    print(f"  -> GAIN ~0 means geometry reduces to convergence+confidence; >0 means non-reducible signal")
+
+
+if __name__ == "__main__":
+    CLV = "/home/yurenh2/rrm/research/flossing/analysis_2x2/clv"
+    for tag, pat in [("Sudoku TRM", f"{CLV}/trm_geom_step58590_n512.npz"),
+                     ("Sudoku HRM", f"{CLV}/hrm_geom_step26040_n512.npz")]:
+        g = glob.glob(pat)
+        if g:
+            go(tag, g[0])
+        else:
+            print(f"[pending] {tag}: {pat} not found yet")