diff --git a/4 Qualifying Exam/2 Writing/1. QE Goals and Outcomes.md b/4 Qualifying Exam/2 Writing/1. QE Goals and Outcomes.md
index ae3b4c7a3..32d297900 100644
--- a/4 Qualifying Exam/2 Writing/1. QE Goals and Outcomes.md	
+++ b/4 Qualifying Exam/2 Writing/1. QE Goals and Outcomes.md	
@@ -95,4 +95,4 @@ Experimentally verifying robustness for implementations of controllers requires
 
 This research will utilize a diffusion model to make generation of unstructured perturbations easier. The generative diffusion model is great at creating new samples with a controlled amount of distortion compared to training data. We will use this feature of the diffusion model to generate unstructured perturbations. The diffusion model consists of two processes: a forward process that degrades inputs with noise, and a reverse process that the learned model attempts to denoise the input. This reverse process will be trained using time and frequency response data of a variety of dynamic systems, so that noise removal creates samples that look like dynamic systems. Finally, we will control the amount of perturbation by controlling how much we destruct the input of a nominal plant with noise. By introducing less noise we create smaller perturbations, while to create large perturbations we do the opposite.
 
-Verifying implementations of controllers has been an onerous task. Perturbations take a significant effort to generate
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+Verifying implementations of controllers has been an onerous task. Perturbations take a significant effort to generate due to the manual nature of current techniques. Generative models have the potential to accelerate this perturbation generation. If successful, having cheaper access to numerous valid perturbations will make robustness verification of control system implementations more accessible. 
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