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diff --git a/diag/selftest_wl.py b/diag/selftest_wl.py
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+++ b/diag/selftest_wl.py
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+"""Validate the 1-WL instrument on canonical graphs BEFORE trusting any decomposition.
+Run: PYTHONPATH=/home/yurenh2/rrog python3 /home/yurenh2/rrog/diag/selftest_wl.py
+"""
+import numpy as np
+from diag import wl, datasets
+
+
+def run():
+    P = datasets.canonical_pairs()
+    names = list(P.keys())
+    adjs = [wl.edges_to_adj(P[k]['n'], P[k]['edge_index']) for k in names]
+    node_rounds, ghist_rounds, conv = wl.wl_refine(adjs)
+    color = {names[i]: ghist_rounds[conv][i] for i in range(len(names))}
+    print("converged round:", conv)
+    for k in names:
+        print(f"  {k:5s} tri={P[k]['tri']}  wlcolor={color[k]}")
+
+    # (1) C6 == 2C3 under 1-WL (both 2-regular) yet differ in triangles -> counting is H2
+    assert color['C6'] == color['2C3'], "C6 vs 2C3 should be 1-WL-equal"
+    assert P['C6']['tri'] != P['2C3']['tri']
+    # (2) P4 != K1,3 (different degree multiset)
+    assert color['P4'] != color['K1,3'], "P4 vs K1,3 should be 1-WL-distinct"
+    print("OK canonical: C6==2C3 (WL blind to triangles), P4!=K1,3")
+
+    # (3) regression H2 floor on {C6,2C3} == target variance
+    sub = [wl.edges_to_adj(P['C6']['n'], P['C6']['edge_index']),
+           wl.edges_to_adj(P['2C3']['n'], P['2C3']['edge_index'])]
+    y = [P['C6']['tri'], P['2C3']['tri']]
+    _, gh, cv = wl.wl_refine(sub)
+    dec = wl.decompose_regression(gh, cv, L=10, y=y, train_idx=[0, 1], eval_idx=[0, 1])
+    print(f"  triangle-count H2 floor MSE on {{C6,2C3}} = {dec['mse_floor_oracle_H2']:.4f} "
+          f"(target var = {np.var(y):.4f})")
+    assert abs(dec['mse_floor_oracle_H2'] - np.var(y)) < 1e-9
+
+    # (4) CSL: 4-regular -> 1 node color, 1 graph color, WL-optimal acc = chance (0.1) -> 100% H2
+    csl = datasets.build_csl(n_per_class=15, seed=0)
+    adjs = [wl.edges_to_adj(d['n'], d['edge_index']) for d in csl]
+    nr, gh, cv = wl.wl_refine(adjs)
+    n_node_colors = len(set(nr[cv][0].tolist()))
+    n_graph_colors = len(set(gh[cv]))
+    y = [d['y'] for d in csl]
+    idx = list(range(len(csl)))
+    att = wl.attribute_classification(gh, cv, L=4, y=y, train_idx=idx, eval_idx=idx)
+    print(f"  CSL: node-colors={n_node_colors}, distinct graph-colors={n_graph_colors}, "
+          f"WL-optimal acc={att['wl_optimal_acc_converged']:.3f} (chance 0.1), buckets={att['counts']}")
+    assert n_node_colors == 1 and n_graph_colors == 1
+    assert abs(att['wl_optimal_acc_converged'] - 0.1) < 1e-6
+    assert att['counts'].get('H2', 0) == len(csl)
+    print("OK CSL: fully 1-WL-collapsed -> 100% of failures are H2. Instrument VALIDATED.")
+
+
+if __name__ == "__main__":
+    run()