diff --git a/train.py b/train.py
index 2e743974..a1936fc9 100644
--- a/train.py
+++ b/train.py
@@ -450,6 +450,10 @@ def step(self):
 DEPTH = 8               # number of transformer layers
 DEVICE_BATCH_SIZE = 128  # per-device batch size (reduce if OOM)
 
+# Early structural triage
+TRIAGE_TIME = 60        # seconds into training; 0 to disable
+TRIAGE_KILL = 0.5       # kill if effective rank drops below this fraction of initial
+
 # ---------------------------------------------------------------------------
 # Setup: tokenizer, model, optimizer, dataloader
 # ---------------------------------------------------------------------------
@@ -531,6 +535,31 @@ def get_muon_momentum(step):
 def get_weight_decay(progress):
     return WEIGHT_DECAY * (1 - progress)
 
+@torch.no_grad()
+def structural_triage(model):
+    """Effective rank (spectral entropy) and gradient coherence of weight matrices."""
+    ranks, grad_groups = [], {}
+    for p in model.parameters():
+        if p.ndim != 2 or min(p.shape) < 64:
+            continue
+        s = torch.linalg.svdvals(p.float())
+        s = s / s.sum()
+        s = s[s > 1e-8]
+        ranks.append(-(s * s.log()).sum().exp().item())
+        if p.grad is not None:
+            grad_groups.setdefault(p.shape, []).append(p.grad.float().flatten())
+    eff_rank = sum(ranks) / len(ranks) if ranks else 0.0
+    sims = []
+    for grads in grad_groups.values():
+        for i in range(len(grads) - 1):
+            sims.append(F.cosine_similarity(grads[i].unsqueeze(0), grads[i+1].unsqueeze(0)).item())
+    coherence = sum(sims) / len(sims) if sims else 0.0
+    return eff_rank, coherence
+
+initial_rank, _ = structural_triage(model)
+triage_done = TRIAGE_TIME <= 0
+print(f"Initial effective rank: {initial_rank:.1f}")
+
 # ---------------------------------------------------------------------------
 # Training loop
 # ---------------------------------------------------------------------------
@@ -551,6 +580,16 @@ def get_weight_decay(progress):
         loss.backward()
         x, y, epoch = next(train_loader)
 
+    # Early structural triage (while gradients are still available)
+    if not triage_done and total_training_time >= TRIAGE_TIME:
+        triage_done = True
+        eff_rank, grad_coherence = structural_triage(model)
+        rank_ratio = eff_rank / initial_rank if initial_rank > 0 else 0
+        print(f"\n[triage@{total_training_time:.0f}s] rank={eff_rank:.1f} ({rank_ratio:.0%} of init) coherence={grad_coherence:.4f}")
+        if rank_ratio < TRIAGE_KILL:
+            print(f"[triage] KILL: effective rank collapsed to {rank_ratio:.0%} of initial")
+            exit(1)
+
     # Progress and schedules
     progress = min(total_training_time / TIME_BUDGET, 1.0)
     lrm = get_lr_multiplier(progress)
@@ -628,3 +667,8 @@ def get_weight_decay(progress):
 print(f"num_steps:        {step}")
 print(f"num_params_M:     {num_params / 1e6:.1f}")
 print(f"depth:            {DEPTH}")
+final_rank, _ = structural_triage(model)
+rank_retention = final_rank / initial_rank if initial_rank > 0 else 0
+print(f"eff_rank_init:    {initial_rank:.1f}")
+print(f"eff_rank_final:   {final_rank:.1f}")
+print(f"rank_retention:   {rank_retention:.4f}")