Ship the learned-key pipeline, without which six figures cannot be rebuilt

The package was missing every script behind the KM (lrn.) curves and
both learned table rows: exp_learned's driver, the merge that folds the
learned rows into sec_compare.csv and refresh_summary.csv, and the
report that reads the learned numbers back. It was also missing
sec_keylen_perm.csv, so Fig. 3 could not be regenerated at all, and the
two diagnostics that answer why a fixed key beats a learned one here and
where a learned mask would win instead.

The learned artifacts themselves are regenerated. They were trained on
the cross-entropy alone, which drifts to disjoint sparse supports: 99
percent of each key's energy on about six of the 64 entries, so a digit
is decided over a sixth of its period and the key set is a choice of
support rather than a dense direction in R^L. They are now the
regularized keys of Section V-C, and check_consistency asserts which of
the two families the figures draw.

Verified from inside this repository: replot_security.py rebuilds all
seven result figures, make_tables.py reproduces both result tables, and
check_consistency.py passes every check that does not need the
manuscript.

The README now lists what ships. Its run list, layout and figure map had
none of the learned pipeline, named two tables the manuscript renders as
prose, and gave Fig. 3 no data file for its permutation curve.

code/exp_full.py

@@ -194,11 +194,14 @@ def stage_N():
     The structured family is enumerable and closed under the elementwise
     product, the learned one is neither, so the paper reports both. This
     stage runs the same SNR sweep as stage_A with the keys trained in
-    R^L instead of frozen to Walsh-Hadamard rows, at the same frame
-    count, so the two are directly comparable.
+    R^L under the regularized loss instead of frozen to Walsh-Hadamard
+    rows, at the same frame count, so the two are directly comparable.
     """
     print("[N] security vs SNR, learned key family ...")
-    m = get_model(P=4, vu=16, d=MAIN_D, U=4, iters=4000, seed=1)
+    # the regularized loss of Section V-C, not the cross-entropy alone:
+    # see exp_learned.learned_model for why the unpenalized keys are not
+    # the family the paper claims
+    m = get_model_reg(P=4, vu=16, d=MAIN_D, U=4, iters=4000, seed=1)
     snr = [float(v) for v in range(0, 21, 2)]
     frames = 800_000
     legit = eval_ser_sse(m, snr, frames=frames)