AUDIT: retract confounded Muon verdict; tone down parity claims (n=3, best-of-noisy-val); scope depth-tax claim to 4k horizon; guard-split skip telemetry (skd/skg); add BP control arm diag_D_bp (+--resume in BP trainer); record resume confounds

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014FAPDWQ49M5Ye3NpTndTpn

1 files changed, 3 insertions, 1 deletions

diff --git a/ep_run/casc_eq_train.py b/ep_run/casc_eq_train.py
index 74d6a3b..b85ac96 100644
--- a/ep_run/casc_eq_train.py
+++ b/ep_run/casc_eq_train.py
@@ -211,6 +211,7 @@ def ep_step(x, y):
         drift = sum(float((a - b).norm()) for a, b in zip(zp, zs_free)) / max(
             sum(float(b.norm()) for b in zs_free), 1e-9)
     if (not math.isfinite(drift)) or (drift > 0.5 and not args.noguard):
+        GOV['skd'] = GOV.get('skd', 0) + 1   # drift-guard reject (relaxation non-convergence)
         for p in all_params: p.grad = None
         return free_ce, beta_t, GOV['K'], False
     GOV['drift'] = drift
@@ -226,6 +227,7 @@ def ep_step(x, y):
     GOV['gema'] = 0.99 * GOV['gema'] + 0.01 * gn        # EMA always updates (frozen-ref bugfix)
     GOV['gn'] = gn
     if not math.isfinite(gn) or (gn > 8 * GOV['gema'] and not args.noguard):
+        GOV['skg'] = GOV.get('skg', 0) + 1   # gn-EMA-guard reject (gradient-magnitude spike)
         for p in all_params: p.grad = None
         return free_ce, beta_t, GOV['K'], False
     for p, g in zip(all_params, gs):
@@ -287,7 +289,7 @@ for step in range(start_step, args.steps + 1):
         val = evaluate(); best = min(best, val)
         gtag = '' if math.isnan(gcos) else f' cos={gcos:.4f}'
         print(f'step {step:5d}/{args.steps} | train {ce:.4f} val {val:.4f} (best {best:.4f}) '
-              f'| beta={beta_t:.2e} K={rounds} skips={skips}{gtag} '
+              f'| beta={beta_t:.2e} K={rounds} skips={skips}(d{GOV.get("skd",0)}/g{GOV.get("skg",0)}){gtag} '
               f'drift={GOV["drift"]:.3f} gn={GOV["gn"]:.2e} sig={GOV["sig"]:.1f} | {step/max(time.time()-t0,1e-9):.3f} it/s', flush=True)
         if wb is not None:
             try: wb.log({'train_ce': ce, 'val_ce': val, 'best': best, 'beta_t': beta_t,