EqOLion R4(真实轨迹判决): 冻结极因子几十步退相关至~0.53=慢漂移前提对符号模式不成立(极均衡

放大病态尾部,小σ奇异向量高速旋转); 子满秩探针NaN, 满秩单迭代仅平基线; 存活空间收窄到温启动
全NS(3-5×)或Occam臂(行/列AGC)升主候选; 限定=最早期轨迹为最难工况, 晚期重dump后再判。
附: --dump_mom探针实装(601步×3矩阵/276MB)。

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

1 files changed, 17 insertions, 0 deletions

diff --git a/ep_run/casc_eq_train.py b/ep_run/casc_eq_train.py
index 7ded9d9..4a436d1 100644
--- a/ep_run/casc_eq_train.py
+++ b/ep_run/casc_eq_train.py
@@ -61,6 +61,9 @@ ap.add_argument('--gen_new', type=int, default=120)
 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('--dump_mom', type=int, default=0)   # >0: record 3 Muon momentum matrices every step
+                                                      # for N steps -> runs/momdump_{tag}.pt, then exit
+                                                      # (EqOLion tracking-falsification input)
 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
@@ -1048,6 +1051,20 @@ for step in range(start_step, args.steps + 1):
             WSNAP['p'] = [p.detach().clone() for p in all_params]
         WSNAP['o'] = _clone_state(opt.state_dict())
     opt.step(); sched.step(); opt.zero_grad(set_to_none=True)
+    if args.dump_mom > 0 and args.opt == 'muon':
+        # per-step momentum trajectory for the EqOLion tracking falsification: three
+        # representative block matrices (low / mid / top), CPU-cloned after each step.
+        om = opt.optimizers[0]
+        _mats = om.param_groups[0]['params']
+        _sel = [_mats[1], _mats[len(_mats) // 2], _mats[-1]]
+        if 'MDUMP' not in globals():
+            MDUMP = {'shapes': [tuple(p.shape) for p in _sel], 'traj': []}
+        MDUMP['traj'].append([om.state[p]['mom'].detach().float().cpu().clone()
+                              for p in _sel if p in om.state and 'mom' in om.state[p]])
+        if step >= args.dump_mom:
+            torch.save(MDUMP, f'runs/momdump_{args.tag}.pt')
+            print(f'[momdump] DONE {len(MDUMP["traj"])} steps -> runs/momdump_{args.tag}.pt', flush=True)
+            import sys; sys.exit(0)
     if args.qup_bits > 0:
         # STAGE-0 HW GATE: finite conductance levels. Snap every weight to an ABSOLUTE
         # per-tensor grid (range/2^bits) with stochastic rounding (unbiased) — emulates