chore: fixed formating and adjusting other components

Makefile

@@ -73,7 +73,7 @@ stats.lines:
 	@find . \( -path '*/node_modules' -o -path '*/.venv' -o -path '*/venv' \) -prune -o \
 	\( -name "*.ts" -o -name "*.py" \) -type f -print0 | xargs -0 cat | wc -l
 
-.PHONY wordcount
+.PHONY: wordcount
 wordcount:
 	@echo "Counting words in main text (excluding appendix)..."
 	@texcount -nosub -total -sum -1 \
@@ -92,4 +92,4 @@ watch: pdf.watch
 run.webapp: web.dev
 test: test.backend
 count-lines: stats.lines
-all: pdf.build
+all: pdf.build

paper/src/chapters/03-methodology.tex

@@ -230,7 +230,7 @@ We employ Good Old-Fashioned AI (GOFAI) heuristics to generate initial weak labe
 \label{sec:tpe}
 
 
-  For both subsets, we model the session dynamics as a Markov Decision Process (MDP) and estimate the transition kernel $\mathcal{T}$. for each respective actor type we define $\hat{\mathcal{T}}_A$ and $\hat{\mathcal{T}}_H$ which are the general transition kernels subject to clustering into $\hat{\mathcal{T}_y^i}$ where $\forall i \in \text{behavioral clusters of } \hat{\mathcal{T}}_y} $. This is done to avoid a lumping of all actor behavior and allows for more intral-class penalization. The probability of transitioning to state $s'$ given state $s$ is estimated via maximum likelihood:
+  For both subsets, we model the session dynamics as a Markov Decision Process (MDP) and estimate the transition kernel $\mathcal{T}$. for each respective actor type we define $\hat{\mathcal{T}}_A$ and $\hat{\mathcal{T}}_H$ which are the general transition kernels subject to clustering into $\hat{\mathcal{T}_y^i}$ where $\forall i \in \text{behavioral clusters of } \hat{\mathcal{T}}_y $. This is done to avoid a lumping of all actor behavior and allows for more intral-class penalization. The probability of transitioning to state $s'$ given state $s$ is estimated via maximum likelihood:
 \begin{equation}
     \hat{P}(s' \mid s) = \frac{N(s, s')}{\sum_{k \in \mathcal{S}} N(s, k)}
 \end{equation}

paper/src/main.tex

@@ -1,29 +1,29 @@
 % -*- TeX-master: t -*-
 \documentclass[12pt,letterpaper]{article}
 
-\pagestyle{plain}
-
 \input{preamble}
 
 \begin{document}
 
-\title{Adversarially Distributionally Robust Optimization and Reinforcement Learning for Informed Dynamic Pricing under Strategic Demand Contamination}
+\begin{titlepage}
-
+    \centering
-\author{
+    \Large\textbf{IE University}\\[0.5cm]
-  Daniel Rösel\thanks{Primary author and student researcher. Email: daniel@alves.world} \\
+    % \includegraphics[width=0.4\textwidth]{graphics/SST.png}\\[1cm]
-  IE University, Madrid, Spain \\[1em]
+    \LARGE\textbf{PHANTOM: Pricing Heuristics Against Non-human Transaction Orchestration Mechanisms}\\[0.5cm]
-  Alberto Martín Izquierdo\thanks{Thesis advisor. Email: amartini@faculty.ie.edu} \\
+    \Large\textbf{Daniel Rösel}\\
-  IE University, Madrid, Spain
+    \large\textit{Bachelor of Computer Science \& Artificial Intelligence}\\[0.5cm]
-}
+    \Large\textit{Supervised by:}\\
-
+    \Large\textbf{Alberto Martín Izquierdo}\\
-\date{\today}
+    \large\textit{IE University, Madrid, Spain}\\[1cm]
-
+    \large\today
-\maketitle
+\end{titlepage}
 
 \begin{abstract}
 The primary objective of this thesis is to develop and validate pricing heuristics that protect e-commerce platforms from systematic exploitation by Large Language Model (LLM) agents within dynamic pricing environments. As AI agents increasingly mediate consumer transactions, they enable users to circumvent the Cost of Information (the price premium accumulated through demand signal expression) by conducting reconnaissance in isolated sessions before executing purchases through clean sessions at base prices. This research will make an anticipatory contribution by adapting recommendation system methodologies to distinguish between genuine human browsing behavior and agent-orchestrated information gathering, thereby enabling pricing systems to maintain margin integrity without degrading the user experience for legitimate customers or getting rid of leads generated by LLMs.
 \end{abstract}
 
+\noindent\textbf{Keywords:} Dynamic Pricing, LLM Agents, Adversarial Machine Learning, E-commerce, Behavioral Detection, Reinforcement Learning
+
 
 \input{chapters/01-intro}
 \input{chapters/02-literature-review}
@@ -35,7 +35,7 @@ The primary objective of this thesis is to develop and validate pricing heuristi
 
 \section*{Acknowledgments}
 Eugene Bykovets, PhD - ETH for helping with problem formulation.
-Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC).
+This research was supported by the TPU Research Cloud program.
 
 \printbibliography
 

paper/src/preamble.tex

@@ -1,6 +1,10 @@
+% Encoding
+\usepackage[utf8]{inputenc}
+
 % Math packages (load before fonts to avoid conflicts)
 \usepackage{amsmath}
 \usepackage{amsthm}
+\usepackage{appendix}
 
 % Define theorem environments
 \newtheorem{theorem}{Theorem}
@@ -53,3 +57,13 @@
 % Use biblatex with authoryear style for in-text citations like (Author, Year)
 \usepackage[backend=bibtex,style=authoryear,natbib=true,maxcitenames=2]{biblatex}
 \addbibresource{bib/references.bib}
+
+% Page headers (SciTech format)
+\usepackage{fancyhdr}
+\setlength{\headheight}{14.5pt}
+\addtolength{\topmargin}{-2.5pt}
+\pagestyle{fancy}
+\fancyhf{}
+\fancyhead[L]{PHANTOM}
+\fancyhead[R]{\thepage}
+\renewcommand{\headrulewidth}{0pt}