RESULT 77: 地板理论三箭全中(fp64塌零/β平移/β×8痊愈) — 物理泄漏改判fp32伪影; --read_lin零成本修复实装(读出用满精度d替代舍入的z−o), C768验证在飞; geo电池撤除; dgain重新定性为数值workaround

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

1 files changed, 7 insertions, 1 deletions

diff --git a/ep_run/casc_eq_train.py b/ep_run/casc_eq_train.py
index 76b96fd..3cb9494 100644
--- a/ep_run/casc_eq_train.py
+++ b/ep_run/casc_eq_train.py
@@ -56,6 +56,9 @@ ap.add_argument('--dgain_rand', type=float, default=0.0)
 ap.add_argument('--probe_dgspec', type=int, default=0)      # >0: M1 spectroscopy, value = n batches; exits before training
 ap.add_argument('--probe_gains', default='1,2,4,8,16,32,64,128,256')
 ap.add_argument('--probe_f64', action='store_true')  # fp64 states+model in the probe: the fp-floor decisive arm  # >1: per-STEP log-uniform dgain_top in
+ap.add_argument('--read_lin', action='store_true')   # linear-form theta-read: cotangent = the stored d tensor (full
+                                                      # precision) instead of (z - o) (an fp32-ROUNDED copy of d);
+                                                      # algebraically identical via the read identity z - o = d
                                                           # [1, this] (spread-spectrum probing of the
                                                           # decade-spread threshold distribution)
 ap.add_argument('--dgain_top', type=float, default=1.0)  # amplify d in STATE FORMATION for blocks
@@ -892,7 +895,10 @@ if args.probe_dgspec > 0:
                     for l in range(args.L):
                         disp[l] += float(GOV['_last_d'][l].norm() / max(float(zp[l].norm()), 1e-12)) / NB
             E = 0.0
-            for z, o in zip(zp, lo): E = E + 0.5 * ((z.detach().to(SDT) - o.to(SDT)) ** 2).sum()
+            if args.read_lin:
+                for dl, o in zip(GOV['_last_d'], lo): E = E - (dl.detach().to(SDT) * o.to(SDT)).sum()
+            else:
+                for z, o in zip(zp, lo): E = E + 0.5 * ((z.detach().to(SDT) - o.to(SDT)) ** 2).sum()
             obj = E / (NBT * bt) + obj_loss(readout(zp[-1].detach()).reshape(-1, vocab), y.reshape(-1))
             gs = torch.autograd.grad(obj, all_params, allow_unused=True)
             for j, ix in enumerate(bix):