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+#!/bin/bash
+#SBATCH --job-name=snn_test
+#SBATCH --account=bfqt-delta-gpu
+#SBATCH --partition=gpuA40x4-interactive
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=4
+#SBATCH --gpus-per-node=1
+#SBATCH --mem=16G
+#SBATCH --time=00:30:00
+#SBATCH --output=runs/slurm_logs/%j_test.out
+#SBATCH --error=runs/slurm_logs/%j_test.err
+
+# ============================================================
+# Quick Test: Verify snnTorch model and Lyapunov computation
+# ============================================================
+# Use interactive partition for fast turnaround
+#
+# Usage:
+#   sbatch scripts/test_interactive.sbatch
+# ============================================================
+
+set -e
+
+PROJECT_DIR="/projects/bfqt/users/yurenh2/ml-projects/snn-training"
+cd "$PROJECT_DIR"
+
+mkdir -p runs/slurm_logs
+
+echo "============================================================"
+echo "Quick Test: SNN with Lyapunov Regularization"
+echo "Job ID: $SLURM_JOB_ID | Node: $SLURM_NODELIST"
+echo "============================================================"
+nvidia-smi --query-gpu=name,memory.total --format=csv,noheader
+echo "============================================================"
+
+# Test 1: Model creation and forward pass
+echo "Test 1: Model and Lyapunov computation..."
+python -c "
+import torch
+import sys
+sys.path.insert(0, '.')
+from files.models.snn_snntorch import LyapunovSNN
+from files.analysis.stability_monitor import StabilityMonitor
+
+device = torch.device('cuda')
+model = LyapunovSNN(input_dim=100, hidden_dims=[128, 64], num_classes=10).to(device)
+x = torch.randn(8, 50, 100, device=device)
+
+logits, lyap, recordings = model(x, compute_lyapunov=True, record_states=True)
+print(f'  Logits shape: {logits.shape}')
+print(f'  Lyapunov exponent: {lyap.item():.4f}')
+print(f'  Spikes shape: {recordings[\"spikes\"].shape}')
+print('  PASSED')
+"
+
+# Test 2: Training loop
+echo ""
+echo "Test 2: Training loop with Lyapunov regularization..."
+python -c "
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import sys
+sys.path.insert(0, '.')
+from files.models.snn_snntorch import LyapunovSNN
+
+device = torch.device('cuda')
+model = LyapunovSNN(input_dim=100, hidden_dims=[128, 64, 32], num_classes=10).to(device)
+optimizer = optim.Adam(model.parameters(), lr=1e-3)
+ce_loss = nn.CrossEntropyLoss()
+
+x = torch.randn(16, 50, 100, device=device)
+y = torch.randint(0, 10, (16,), device=device)
+
+for step in range(5):
+    optimizer.zero_grad()
+    logits, lyap, _ = model(x, compute_lyapunov=True, record_states=False)
+    loss = ce_loss(logits, y) + 0.1 * (lyap - 0.0) ** 2
+    loss.backward()
+    optimizer.step()
+    print(f'  Step {step+1}: loss={loss.item():.4f}, lyap={lyap.item():.4f}')
+
+print('  PASSED')
+"
+
+# Test 3: Depth comparison (minimal)
+echo ""
+echo "Test 3: Depth comparison (2 epochs, depths 1,2,4)..."
+python files/experiments/depth_comparison.py \
+    --synthetic \
+    --epochs 2 \
+    --depths 1 2 4 \
+    --hidden_dim 64 \
+    --out_dir runs/test_output \
+    --device cuda \
+    --no-progress
+
+echo ""
+echo "============================================================"
+echo "All tests PASSED"
+echo "============================================================"