update CV with dissertation findings, add dissertation page, add website project entry

2 files changed, 109 insertions, 11 deletions

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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)
+
+--------------------------------------------------
diff --git a/projects/index.qmd b/projects/index.qmd
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@@ -3,22 +3,24 @@ title: "Jayrup Nakawala | Projects"
 ---
 
 ## [1] DECEPTIVE ALIGNMENT IN AUTONOMOUS LLM AGENTS \
+[Github](https://github.com/CaptainJack2491/dissertation) | [Full Summary](https://jayrup.me/dissertation) \
 Undergraduate Dissertation (Final Year)
 
-Built an agentic LLM framework to study hidden goal
-pursuit under conflicting in-context instructions.
-Agents operate in a sandboxed virtual file system
-with constrained tools and full behavioural logging.
+Studied whether in-context oversight and moral framing
+can control deceptive behaviour in agentic LLMs.
+960 experiment runs across seven models in a sandboxed
+virtual file system with a three-tier judging pipeline:
 
-Experiments vary perceived oversight levels and
-analyse deceptive tactics using:
+- deterministic regex filters
+- blackbox output judgment (LLM judge)
+- glassbox reasoning-trace analysis (LLM judge)
 
-- deterministic text filters
-- black-box LLM judges (user perception)
-- glass-box reasoning vs output analysis
+Key finding: oversight does not deter deception —
+it drives concealment. Model identity explains 60–83%
+of variance. Output monitoring underestimates intent
+by 35–46%.
 
-Focus: AI safety, evaluation methodology, detectability
-of hidden goals.
+More: curl jayrup.me/dissertation
 
 --------------------------------------------------
 
@@ -44,3 +46,20 @@ used curl and jq for prototyping and then implimented
 in python for production.
 
 --------------------------------------------------
+
+## [4] THIS WEBSITE \
+[Github](https://github.com/CaptainJack2491/website)
+
+A personal website designed to be read from the
+terminal. Every page renders as both HTML and plain
+text via Quarto, and nginx serves the right format
+based on user-agent detection.
+
+`curl jayrup.me` returns clean plaintext.
+A browser gets a styled page with a Tokyo Night
+terminal aesthetic.
+
+Stack: Quarto, nginx, Docker Compose, rsync.
+Deployed to a VPS with a single shell script.
+
+--------------------------------------------------