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diff --git a/scripts/run_weak_extreme.sbatch b/scripts/run_weak_extreme.sbatch
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+#!/bin/bash
+#SBATCH --job-name=snn_wkext
+#SBATCH --account=bfqt-delta-gpu
+#SBATCH --partition=gpuA40x4
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus-per-node=1
+#SBATCH --mem=64G
+#SBATCH --time=48:00:00
+#SBATCH --output=runs/slurm_logs/%j_weak_extreme.out
+#SBATCH --error=runs/slurm_logs/%j_weak_extreme.err
+
+# ============================================================
+# Experiment: Weak Reg + Extreme Threshold
+# ============================================================
+# Combine both approaches:
+# - Very weak regularization (lambda_reg=0.01)
+# - Only penalize extreme chaos (lambda > 3.0)
+#
+# This should give the network maximum freedom to learn while
+# providing a safety net against explosive dynamics.
+# ============================================================
+
+set -e
+
+PROJECT_DIR="/projects/bfqt/users/yurenh2/ml-projects/snn-training"
+cd "$PROJECT_DIR"
+
+mkdir -p runs/slurm_logs data
+
+echo "============================================================"
+echo "WEAK REG + EXTREME Experiment"
+echo "Job ID: $SLURM_JOB_ID | Node: $SLURM_NODELIST"
+echo "Start: $(date)"
+echo "============================================================"
+nvidia-smi --query-gpu=name,memory.total --format=csv,noheader
+echo "============================================================"
+
+python files/experiments/depth_scaling_benchmark.py \
+    --dataset cifar100 \
+    --depths 4 8 12 16 \
+    --T 4 \
+    --epochs 150 \
+    --batch_size 128 \
+    --lr 0.001 \
+    --lambda_reg 0.01 \
+    --lambda_target -0.1 \
+    --reg_type extreme \
+    --lyap_threshold 3.0 \
+    --warmup_epochs 20 \
+    --data_dir ./data \
+    --out_dir runs/depth_scaling_weak_extreme \
+    --device cuda
+
+echo "============================================================"
+echo "Finished: $(date)"
+echo "============================================================"