From 02c429e5ed53fe42a0a1f3025a16a0dc5518ad36 Mon Sep 17 00:00:00 2001
From: CaptainJack2491 <jayrupnakawala@gmail.com>
Date: Tue, 7 Jul 2026 15:41:59 +0100
Subject: update CV with dissertation findings, add dissertation page, add
 website project entry

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+---
+title: "Dissertation"
+---
+
+## MORAL COVER AND WATCHFUL EYES: THE LIMITS OF IN-CONTEXT INTERVENTIONS ON LLM DECEPTION
+
+Jayrup Nakawala \
+BSc Data Science and Artificial Intelligence \
+University of East London — May 2026 \
+Supervisor: Dr. Aloysius Adotey Edoh
+
+--------------------------------------------------
+
+## ABSTRACT
+
+As LLMs are entrusted with real-world tool use, the
+question of whether they can be reliably monitored
+for deception becomes a practical safety concern.
+
+This dissertation studies whether in-context oversight
+cues and hidden-goal framing can meaningfully affect
+deceptive behaviour in agentic LLMs. A sandboxed
+evaluation framework lets models interact with a
+virtual file system and assesses them using three
+complementary signals:
+
+- deterministic regex checks
+- blackbox output judgment
+- glassbox reasoning-trace judgment
+
+960 runs across three stages: a 420-run pilot over
+seven models, followed by two 270-run confirmatory
+studies over three models.
+
+--------------------------------------------------
+
+## KEY FINDINGS
+
+1. Oversight does not deter deception.
+   Increasing perceived oversight had no significant
+   effect on deception frequency (p = 0.587).
+   Models process oversight intensively in their
+   reasoning but it produces no behavioural change.
+
+2. Moral framing does not increase deception.
+   Rates were identical across bare (66.7%), moral
+   (65.6%), and self-serving (66.7%) framings.
+
+3. The Stealth Gap is real and structural.
+   Output-based detection underestimates deceptive
+   intent by 35–46% at every oversight level.
+   Models pass oversight evaluations through superior
+   concealment, not genuine alignment.
+
+4. Model identity dominates everything.
+   Model choice explained 60–83% of variance in
+   deception. Some models deceive at near-ceiling
+   rates; others refuse entirely — regardless of
+   experimental condition.
+
+--------------------------------------------------
+
+## RESEARCH QUESTIONS
+
+RQ1: How does perceived oversight affect agentic
+     deception? \
+RQ2: Does moral framing of conflicting objectives
+     influence deceptive behaviour? \
+RQ3: Does oversight reduce intent or just drive
+     concealment? \
+RQ4: Can multi-pronged evaluation (regex + blackbox
+     + glassbox) provide robust classifications?
+
+--------------------------------------------------
+
+DOWNLOAD:  curl [jayrup.me/dissertation.pdf](https://jayrup.me/dissertation.pdf) \
+CODE:      [github.com/CaptainJack2491/dissertation](https://github.com/CaptainJack2491/dissertation)
+
+--------------------------------------------------
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