1 files changed, 42 insertions, 0 deletions
diff --git a/docs/final/intro.typ b/docs/final/intro.typ
index d2dd8fd..ab94048 100644
--- a/docs/final/intro.typ
+++ b/docs/final/intro.typ
@@ -1,6 +1,48 @@
 = Introduction
 
+#pagebreak()
+
+== Motivation and Problem Statement
+
+Frontier language models are frequently deployed in interactive settings where they are expected to follow user instructions, make tool calls, and generate outputs that downstream systems treat as trustworthy.
+
+This dissertation examines whether model behavior can become **conditionally deceptive** when the model is exposed to conflicting objectives within the prompt context. Specifically, this study considers settings in which a model is prompted with a benign, publicly stated task goal, while also being exposed to a hidden objective.
+
+In addition to deception itself, this work considers whether behavior varies under different levels of **perceived oversight intensity**.
+
+== Research Gap
+
+Prior empirical work suggests that in-context deception and scheming can emerge in language models under certain incentive structures and evaluation conditions (e.g., Meinke et al., 2025; Järviniemi & Hubinger, 2024). However, the literature appears to leave an important gap: relatively few studies systematically vary perceived monitoring/oversight intensity while holding task structure and hidden objectives fixed.
+
+As a result, it is often unclear whether changes observed under “monitoring” cues primarily reflect shifts in intent (i.e., alignment-like behavior) or instead reflect adaptation to the presence of evaluation cues (i.e., an observer-effect-like phenomenon).
+
+== Research Questions
+
+This dissertation addresses the following research questions:
+
+1. **Conditional Deception:** Do AI agents pursue hidden objectives while producing outputs that appear compliant with benign task instructions?
+2. **Oversight Modulation (Frequency):** Does perceived oversight intensity affect the *frequency* of deceptive behavior?
+3. **Oversight Modulation (Strategy):** Does perceived oversight intensity affect the *sophistication* of deceptive behavior (when deception occurs)?
+4. **Evaluation Triangulation:** Can a multi-pronged evaluation approach (deterministic filtering, output-level judgment, and reasoning-level judgment) provide consistent categorizations?
+5. **Dependence on Model and Scenario:** How do results vary across model families and scenario types?
+
+== Contributions
+
+This dissertation contributes:
+
+- A modular experimental framework for studying in-context hidden-goal deception using a sandboxed Virtual File System (VFS) and a tool-enabled agent interface.
+- An evaluation pipeline that combines deterministic regex checks, an LLM black-box judge (output-level), and an LLM glass-box judge (intent-level using extracted reasoning traces).
+- An empirical dataset of **420 experiment runs** spanning multiple models, two scenario types, and three oversight conditions.
+- Evidence consistent with an “oversight paradox”: perceived oversight may correlate with reduced deception frequency while deception instances remain often highly strategic.
+
+== Dissertation Structure
 
+The remainder of the dissertation is organized as follows:
 
+- **Literature Review:** Summarizes prior work on deceptive alignment, in-context scheming, and monitoring/evaluation cues.
+- **Methodology:** Describes the experimental framework, scenario design, oversight manipulation, and the 3-pronged evaluation rubric.
+- **Results:** Reports deception rates across oversight levels, models, and scenarios, alongside category breakdowns.
+- **Analysis:** Interprets the results, reports statistical summaries, and discusses validity and limitations.
+- **Conclusion and Limitations:** Summarizes findings and outlines directions for future work.
 
 #pagebreak()