diff --git a/4 Qualifying Exam/2 Writing/2. QE State of the Art.md b/4 Qualifying Exam/2 Writing/2. QE State of the Art.md
index f1674b88e..0256eee03 100644
--- a/4 Qualifying Exam/2 Writing/2. QE State of the Art.md	
+++ b/4 Qualifying Exam/2 Writing/2. QE State of the Art.md	
@@ -51,10 +51,12 @@ I got ChatGPT to help me with some stuff following the first version: Here was a
 ## ChatGPT Analysis
 ### **Robust Control Theory and Perturbation Challenges**
 **1.1 Robust Control Principles**: Briefly introduce robust control, emphasizing its focus on stability and performance despite uncertainties. Clarify that robust control aims to ensure reliability under a range of conditions.
+	- Where did robust control come from?
 **1.2 Types of Uncertainty in Control Systems**: Explain structured vs. unstructured uncertainties and why unstructured uncertainties, though useful for capturing unmodeled dynamics, are challenging to generate.
 	- Talk about structured first. Structured makes sense bc it's tractable and is intuitive
 	- Then why unstructured. Need to make room for unmodeled dynamics.
 **1.3 Limitations of Current Perturbation Methods**: Discuss the current methods for structured perturbation generation and the lack of effective techniques for generating random, unstructured perturbations, especially for validating controller implementations.
+**1.4 Modern Efforts such as reachability and formal methods**
 ### **Generative Models and Their Potential in Engineering**
 **2.1 Evolution of Generative Modeling**: Provide a brief history of generative models, covering GANs, VAEs, and diffusion models, highlighting their application in creating realistic samples.
 **2.2 Limitations of Existing Models for Control**: Explain why traditional generative models are impractical for control applications, especially where controllable, unstructured perturbations are required.