diff --git a/paper/src/chapters/01-intro.tex b/paper/src/chapters/01-intro.tex
index 2df2f27..f5f2fe8 100644
--- a/paper/src/chapters/01-intro.tex
+++ b/paper/src/chapters/01-intro.tex
@@ -27,11 +27,12 @@ We formally define interaction data as coming from some actor which can either b
 
 \subsection{Research Questions}
 
-This work addresses three core research questions:
+This dissertation is organized around one main research question and three supporting sub-questions:
 \begin{enumerate}
-    \item[\textbf{RQ1}] \textit{Separability}: Can agent and human sessions be reliably distinguished from behavioral interaction signals alone, without relying on network-level or device fingerprinting?
-    \item[\textbf{RQ2}] \textit{Theoretical Impact}: What is the formal relationship between agent contamination levels and the erosion of pricing power in dynamic pricing systems?
-    \item[\textbf{RQ3}] \textit{Robust Mitigation}: How can pricing policies be constructed to maintain margin integrity under unknown and non-stationary levels of agent contamination?
+    \item[\textbf{Main RQ}] How can dynamic pricing systems preserve margin integrity when transaction orchestration is increasingly mediated by non-human agents?
+    \item[\textbf{SQ1}] \textit{Separability}: Can agent and human sessions be reliably distinguished from behavioral interaction signals alone, without relying on network-level or device fingerprinting?
+    \item[\textbf{SQ2}] \textit{Theoretical Impact}: What is the formal relationship between agent contamination levels and the erosion of pricing power in dynamic pricing systems?
+    \item[\textbf{SQ3}] \textit{Robust Mitigation}: How can pricing policies be constructed to maintain margin integrity under unknown and non-stationary levels of agent contamination?
 \end{enumerate}
 
 
diff --git a/paper/src/main.tex b/paper/src/main.tex
index 3680ac8..bcce09e 100644
--- a/paper/src/main.tex
+++ b/paper/src/main.tex
@@ -27,7 +27,7 @@ These behavioral signals serve as inputs for a Distributionally Robust Reinforce
 \noindent\textbf{Keywords:} Dynamic Pricing, LLM Agents, Adversarial Machine Learning, E-commerce, Behavioral Detection, Reinforcement Learning
 
 \vspace{1em}
-\noindent\textbf{Acknowledgments:} This research was supported by the TPU Research Cloud program.
+\noindent\textbf{Acknowledgments:} This research was supported by the TPU Research Cloud program, which provided access to Google Cloud TPU accelerators (including TPU v2/v3/v4).
 
 \clearpage
 \input{chapters/01-intro}