Ciphertext-only family enumeration, and checks that reproduce off a GPU

check_family_enum.py measures the attack the manuscript now states in
Section III-A: the winning correlation is an index-free verifier, so
ranking the 63 non-constant Walsh rows by mean winning correlation
recovers the user set from one frame in 0.905 of 200 trials at 10 dB
and from four frames in 0.990, using nothing outside the stated threat
model. Under the invariance refresh it recovers it in none, because the
entry permutation relabels the codebook the adversary must align
against.

V8 and V9 read the trained codebook through main_model(), which
retrains on every call, and a codebook trained on CUDA is not the one
trained on CPU. The shipped verify_math.csv therefore read PASS here
and FAIL for anyone running this package without a GPU. model_main.pt
is 7 KB and fixes the codebook, which is what both checks are about;
delete it to retrain. V1-V11 now pass on both.

New checks: V10, the format-matched OMA reference Section VI-B quotes,
and V11, the closed-form against Monte Carlo comparison the manuscript
claimed and never stored. V3a's bias-linearity result was computed and
printed but never written to the CSV, so the one linearity claim the
paper quotes was the one this package could not show.

check_consistency.py gains 21 assertions, covering five data files that
no assertion read (users, csi, semantic, cov_attack, sec_jam) and the
trend claims it structurally could not see, since it compared values
and not shapes.

README: the figure map named stages that do not write the artifacts
they list, so following it did not reproduce Figs. 4 and 6; the
reproduction block was five scripts short; and the refresh numbers were
from a superseded run (nearly three, 15.0 to 64.8 bits) against the
manuscript's 2.3 and 23.8 to 364.6.

code/check_consistency.py

@@ -268,6 +268,92 @@ chk("secrecy rate 14.87 of 14.93",
     "%s of %s" % (it["secrecy_rate_refresh_bits"], it["mi_legit_bits"]))
 
 
+_fe = {(float(r["snr_db"]), int(r["n_frames"]), r["keying"]): float(r["recovery"])
+       for r in rows("family_enum.csv")}
+chk("family enumeration recovers the user set at 10 dB",
+    abs(_fe[(10.0, 1, "fixed")] - 0.905) < 5e-3
+    and abs(_fe[(10.0, 4, "fixed")] - 0.990) < 5e-3,
+    "N=1 %.3f, N=4 %.3f" % (_fe[(10.0, 1, "fixed")],
+                            _fe[(10.0, 4, "fixed")]))
+chk("the refresh defeats the family enumeration",
+    _fe[(10.0, 2, "refreshed")] == 0.0,
+    "%.3f over 200 blocks" % _fe[(10.0, 2, "refreshed")])
+
+# --- trends, which the value assertions above cannot see ---------------
+_snr = rows("sec_snr.csv")
+_lg = [float(r["legit"]) for r in _snr]
+chk("legitimate SER falls monotonically with SNR",
+    all(a > b for a, b in zip(_lg, _lg[1:])), "%d points" % len(_lg))
+chk("legitimate below the binary OMA reference at every SNR",
+    all(float(r["legit"]) < float(r["oma"]) for r in _snr),
+    "min margin %.3f" % min(1 - float(r["legit"]) / float(r["oma"])
+                            for r in _snr))
+_kl = rows("sec_keylen.csv")
+chk("legitimate SER falls monotonically with key length",
+    all(float(a["legit_ser"]) > float(b["legit_ser"]) for a, b in zip(_kl, _kl[1:])),
+    "%d lengths" % len(_kl))
+chk("legitimate surpasses the reference from L=16 onward",
+    all(float(r["legit_ser"]) < float(r["oma"]) for r in _kl
+        if r["oma"] != "nan" and int(float(r["L"])) >= 16),
+    "checked L>=16")
+_ter = rows("real_sec_ter.csv")
+chk("legitimate TER below OMA over the whole range",
+    all(float(r["ter_legit"]) < float(r["ter_oma"]) for r in _ter),
+    "%d points" % len(_ter))
+chk("outsider TER stays above 0.9991",
+    min(float(r["ter_eve"]) for r in _ter) > 0.9991,
+    "min %.6f" % min(float(r["ter_eve"]) for r in _ter))
+
+# --- files no assertion read ------------------------------------------
+_us = rows("users.csv")
+chk("keys stay exactly orthogonal at every load",
+    all(float(r["mask_xcorr"]) == 0.0 for r in _us),
+    "U up to %s" % _us[-1]["users"])
+chk("eavesdropper never leaves chance across the load sweep",
+    all(float(r["eve_ser"]) > 0.999 for r in _us),
+    "min %.6f" % min(float(r["eve_ser"]) for r in _us))
+_u = {r["users"]: r for r in _us}
+chk("load endpoints 0.027 and 0.946",
+    abs(float(_u["2"]["legit_ser"]) - 0.027) < 5e-4
+    and abs(float(_u["32"]["legit_ser"]) - 0.946) < 5e-4,
+    "%.4f, %.4f" % (float(_u["2"]["legit_ser"]),
+                    float(_u["32"]["legit_ser"])))
+chk("the OMA crossing lies between U=16 and U=32",
+    float(_u["16"]["legit_ser"]) < float(_u["16"]["oma"])
+    and float(_u["32"]["legit_ser"]) > float(_u["32"]["oma"]),
+    "16: %.3f<%.3f, 32: %.3f>%.3f"
+    % (float(_u["16"]["legit_ser"]), float(_u["16"]["oma"]),
+       float(_u["32"]["legit_ser"]), float(_u["32"]["oma"])))
+_csi = rows("csi.csv")
+chk("phase residual moves the rate to 0.057 at 0.2 rad",
+    any(abs(float(r["legit_ser"]) - 0.057) < 1e-3 for r in _csi),
+    "%d rows" % len(_csi))
+_sem = rows("semantic.csv")
+chk("legitimate similarity at least 0.96 in both spaces",
+    all(float(r["legit"]) >= 0.96 for r in _sem if r["snr_db"] == "10.0"),
+    "%d rows" % len(_sem))
+_cov = rows("cov_attack.csv")
+chk("covariance attack reaches 0.26 at 300 same-key frames",
+    any(r["n_frames"] == "300" and abs(float(r["eve_ser"]) - 0.26) < 0.01
+        for r in _cov),
+    "%d rows" % len(_cov))
+chk("unjammed reference is 0.053",
+    abs(col("sec_jam.csv", "nojam")[0] - 0.053) < 0.05, "sec_jam.csv read")
+
+# --- the closed-form checks the manuscript quotes ----------------------
+_vm = {r["check"]: r for r in rows("verify_math.csv")}
+for _k, _c in [("V8 cross-period remainder", 0.0005),
+               ("V9 score-variance ratio", 0.05),
+               ("V10 format-matched OMA at 10 dB", 0.001),
+               ("V3a bias slope in kappa", 0.03)]:
+    chk("stored check %s passes" % _k.split()[0],
+        _k in _vm and _vm[_k]["verdict"] == "PASS"
+        and float(_vm[_k]["abs_err"]) <= _c,
+        _vm[_k]["empirical"] if _k in _vm else "row missing")
+chk("format-matched OMA quoted as 0.055",
+    "$0.055$ at $10$~dB against the proposed" in tex, "Section VI-B",
+    needs_tex=True)
+
 # --- tables against their generator -----------------------------------
 # Every printed table cell must be the one make_tables.py derives from
 # data/, so a rerun that moves a number cannot leave the manuscript behind.