diff --git a/report/report.tex b/report/report.tex
index 603a549..c25b576 100644
--- a/report/report.tex
+++ b/report/report.tex
@@ -61,9 +61,11 @@ User & j.thompson@example.com & password \\
 \end{tabular}
 \end{center}
 
-\textit{Note: I will try to keep the public website available, but the GPU node runs on my home PC and may occasionally go offline. For reliable testing, I recommend running the system locally on a machine with a GPU.}
+\textit{Note: I will try to keep the public website available, but the GPU node 
+runs on my home PC and may occasionally go offline. For reliable testing, 
+I recommend running the system locally on a machine with a CUDA-enabled GPU.}
 
-Manager registration code (if required): \texttt{MANAGER2026}
+Manager registration code (for signup): \texttt{MANAGER2026}
 
 \section{Introduction}\label{introduction}
 
@@ -268,6 +270,26 @@ APIs, supports offline or air-gapped environments, and aligns with
 enterprise privacy requirements while maintaining acceptable inference
 performance.
 
+\subsection{Positioning Against Alternative
+Approaches}\label{positioning-against-alternative-approaches}
+
+Dynavera was designed against three practical alternatives. First,
+human-only onboarding preserves expert nuance but does not scale well
+and introduces recurring opportunity cost for senior staff. Second,
+static LMS/document-first onboarding scales distribution but provides
+limited adaptivity, weak context grounding during Q\&A, and little
+operational traceability beyond completion events. Third, a single
+general chatbot can improve interactivity, but it typically blends
+curriculum, retrieval, assessment, and monitoring concerns into one
+prompt surface, making governance and iterative improvement harder.
+
+The Dynavera architecture chooses a middle path: specialized agent roles
+within one orchestrated runtime, retrieval-grounded generation, and
+persisted session state for reviewability. This trade-off accepts added
+system complexity in exchange for improved modularity, clearer
+responsibility boundaries, and stronger alignment between training
+delivery and management oversight.
+
 \subsection{Learning Origins}\label{learning-origins}
 
 The design and implementation of Dynavera synthesize concepts developed
@@ -560,8 +582,18 @@ During guided learning, module content generation, context retrieval,
 and assessment output are coordinated in sequence. The curriculum phase
 determines structure, the knowledge phase builds role-grounded
 instructional content, and the assessment phase constructs evaluative
-checkpoints. Progress monitoring then summarizes current status using
-persisted session state and completed interactions. This keeps learning
+checkpoints. Final assessment grading follows a mixed strategy: multiple
+choice responses are deterministically compared against configured
+correct options, while non-multiple-choice responses are agent-graded.
+Per-question grading outcomes are persisted in session state for review
+and feedback rendering.
+
+Progress monitoring then summarizes current status using persisted
+session state and completed interactions. In the implemented UI path,
+AI monitor inference is only triggered after onboarding completion;
+before completion, the system presents a local progress summary.
+When available, monitor judgements are informed by prior final-quiz
+question/answer evidence and saved grading details. This keeps learning
 flow adaptive without abandoning traceability.
 
 Finally, workflow state is persisted throughout execution: user