diff --git a/paper/paper_a_results_v3.md b/paper/paper_a_results_v3.md
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--- a/paper/paper_a_results_v3.md
+++ b/paper/paper_a_results_v3.md
@@ -2,9 +2,10 @@
 
 ## A. Experimental Setup
 
-All experiments were conducted on a workstation equipped with an Apple Silicon processor with Metal Performance Shaders (MPS) GPU acceleration.
+Experiments used mixed hardware: YOLOv11n training and inference for signature detection, and ResNet-50 forward inference for feature extraction over all 182,328 detected signatures, were performed on an Nvidia RTX 4090 (CUDA); the downstream statistical analyses (KDE antimode, Hartigan dip test, Beta-mixture EM with logit-Gaussian robustness check, 2D Gaussian mixture, Burgstahler-Dichev/McCrary density-smoothness diagnostic, and pairwise cosine/dHash computations) were performed on an Apple Silicon workstation with Metal Performance Shaders (MPS) acceleration.
 Feature extraction used PyTorch 2.9 with torchvision model implementations.
 The complete pipeline---from raw PDF processing through final classification---was implemented in Python.
+Because all steps rely on deterministic forward inference over fixed pre-trained weights (no fine-tuning) plus fixed-seed numerical procedures, reported results are platform-independent to within floating-point precision.
 
 ## B. Signature Detection Performance