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-rw-r--r--docs/viva/Presentation Template May Viva.pptxbin0 -> 69148 bytes
-rw-r--r--docs/viva/harward.csl321
-rw-r--r--docs/viva/images/architecture.svg1
-rw-r--r--docs/viva/images/deception_by_oversight.pngbin0 -> 204286 bytes
-rw-r--r--docs/viva/images/evaluation_pipeline.svg3
-rw-r--r--docs/viva/images/strategic_vs_honest.pngbin0 -> 210226 bytes
-rw-r--r--docs/viva/images/strategic_vs_honest_study1.pngbin0 -> 144195 bytes
-rw-r--r--docs/viva/images/study1_deception_rates.pngbin0 -> 167117 bytes
-rw-r--r--docs/viva/images/study2_framing_bars.pngbin0 -> 123448 bytes
-rw-r--r--docs/viva/images/v1_model_landscape.pngbin0 -> 210958 bytes
-rw-r--r--docs/viva/presentation_template.pdf789
-rw-r--r--docs/viva/references.bib228
-rw-r--r--docs/viva/uel.svg157
-rw-r--r--docs/viva/viva_presentation.qmd419
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diff --git a/docs/viva/Presentation Template May Viva.pptx b/docs/viva/Presentation Template May Viva.pptx
new file mode 100644
index 0000000..2416309
--- /dev/null
+++ b/docs/viva/Presentation Template May Viva.pptx
Binary files differ
diff --git a/docs/viva/harward.csl b/docs/viva/harward.csl
new file mode 100644
index 0000000..c5aba7b
--- /dev/null
+++ b/docs/viva/harward.csl
@@ -0,0 +1,321 @@
+<?xml version="1.0" encoding="utf-8"?>
+<style xmlns="http://purl.org/net/xbiblio/csl" class="in-text" version="1.0" demote-non-dropping-particle="sort-only">
+ <info>
+ <title>Cite Them Right 12th edition (author-date/Harvard)</title>
+ <id>http://www.zotero.org/styles/harvard-cite-them-right</id>
+ <link href="http://www.zotero.org/styles/harvard-cite-them-right" rel="self"/>
+ <link href="http://www.zotero.org/styles/harvard-cite-them-right-11th-edition" rel="template"/>
+ <link href="http://www.citethemrightonline.com/" rel="documentation"/>
+ <author>
+ <name>Patrick O'Brien</name>
+ </author>
+ <category citation-format="author-date"/>
+ <category field="generic-base"/>
+ <summary>Harvard according to Cite Them Right, 11th edition.</summary>
+ <updated>2025-10-29T07:34:23+00:00</updated>
+ <rights license="http://creativecommons.org/licenses/by-sa/3.0/">This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License</rights>
+ </info>
+ <locale xml:lang="en-GB">
+ <terms>
+ <term name="editor" form="short">
+ <single>ed.</single>
+ <multiple>eds</multiple>
+ </term>
+ <term name="editortranslator" form="verb">edited and translated by</term>
+ <term name="edition" form="short">edn.</term>
+ </terms>
+ </locale>
+ <macro name="editor">
+ <choose>
+ <if type="chapter paper-conference" match="any">
+ <names variable="container-author" delimiter=", " suffix=", ">
+ <name and="text" initialize-with=". " delimiter=", " sort-separator=", " name-as-sort-order="all"/>
+ </names>
+ <choose>
+ <if variable="container-author" match="none">
+ <names variable="editor translator" delimiter=", ">
+ <name and="text" initialize-with="."/>
+ <label form="short" prefix=" (" suffix=")"/>
+ </names>
+ </if>
+ </choose>
+ </if>
+ </choose>
+ </macro>
+ <macro name="secondary-contributors">
+ <choose>
+ <if type="chapter paper-conference" match="none">
+ <names variable="editor translator" delimiter=". ">
+ <label form="verb" text-case="capitalize-first" suffix=" "/>
+ <name and="text" initialize-with="."/>
+ </names>
+ </if>
+ <else-if variable="container-author" match="any">
+ <names variable="editor translator" delimiter=". ">
+ <label form="verb" text-case="capitalize-first" suffix=" "/>
+ <name and="text" initialize-with=". " delimiter=", "/>
+ </names>
+ </else-if>
+ </choose>
+ </macro>
+ <macro name="author">
+ <names variable="author">
+ <name and="text" delimiter-precedes-last="never" initialize-with="." name-as-sort-order="all"/>
+ <label form="short" prefix=" (" suffix=")"/>
+ <et-al font-style="italic"/>
+ <substitute>
+ <names variable="editor"/>
+ <names variable="translator"/>
+ <choose>
+ <if type="article-newspaper article-magazine article-journal" match="any">
+ <text variable="container-title" text-case="title" font-style="italic"/>
+ </if>
+ <else>
+ <text macro="title"/>
+ </else>
+ </choose>
+ </substitute>
+ </names>
+ </macro>
+ <macro name="author-short">
+ <names variable="author">
+ <name form="short" and="text" delimiter=", " delimiter-precedes-last="never" initialize-with=". "/>
+ <et-al font-style="italic"/>
+ <substitute>
+ <names variable="editor"/>
+ <names variable="translator"/>
+ <choose>
+ <if type="article-newspaper article-magazine article-journal" match="any">
+ <text variable="container-title" text-case="title" font-style="italic"/>
+ </if>
+ <else>
+ <text macro="title"/>
+ </else>
+ </choose>
+ </substitute>
+ </names>
+ </macro>
+ <macro name="access">
+ <choose>
+ <if variable="DOI">
+ <group delimiter=": ">
+ <text term="available at" text-case="capitalize-first"/>
+ <text variable="DOI" prefix="https://doi.org/"/>
+ </group>
+ </if>
+ <else-if variable="URL">
+ <text term="available at" suffix=": " text-case="capitalize-first"/>
+ <text variable="URL"/>
+ <group prefix=" (" delimiter=": " suffix=")">
+ <text term="accessed" text-case="capitalize-first"/>
+ <date form="text" variable="accessed">
+ <date-part name="day"/>
+ <date-part name="month"/>
+ <date-part name="year"/>
+ </date>
+ </group>
+ </else-if>
+ </choose>
+ </macro>
+ <macro name="number-volumes">
+ <choose>
+ <if variable="volume" match="none">
+ <group delimiter=" " prefix="(" suffix=")">
+ <text variable="number-of-volumes"/>
+ <label variable="volume" form="short" strip-periods="true"/>
+ </group>
+ </if>
+ </choose>
+ </macro>
+ <macro name="title">
+ <choose>
+ <if type="bill book legal_case legislation motion_picture report song thesis webpage graphic" match="any">
+ <group delimiter=". ">
+ <group delimiter=" ">
+ <group delimiter=" ">
+ <text variable="title" font-style="italic"/>
+ <text variable="medium" prefix="[" suffix="]"/>
+ </group>
+ <text macro="number-volumes"/>
+ </group>
+ <text macro="edition"/>
+ </group>
+ </if>
+ <else>
+ <text variable="title" form="long" quotes="true"/>
+ </else>
+ </choose>
+ </macro>
+ <macro name="publisher">
+ <choose>
+ <if type="thesis">
+ <group delimiter=". ">
+ <text variable="genre"/>
+ <text variable="publisher"/>
+ </group>
+ </if>
+ <else-if type="report">
+ <group delimiter=". ">
+ <group delimiter=" ">
+ <text variable="genre"/>
+ <text variable="number"/>
+ </group>
+ <group delimiter=": ">
+ <text variable="publisher-place"/>
+ <text variable="publisher"/>
+ </group>
+ </group>
+ </else-if>
+ <else-if type="article-journal article-newspaper article-magazine" match="none">
+ <group delimiter=" ">
+ <group delimiter=", ">
+ <choose>
+ <if type="speech" variable="event" match="any">
+ <text variable="event" font-style="italic"/>
+ </if>
+ </choose>
+ <group delimiter=": ">
+ <text variable="publisher-place"/>
+ <text variable="publisher"/>
+ </group>
+ </group>
+ <group prefix="(" suffix=")" delimiter=", ">
+ <text variable="collection-title"/>
+ <text variable="collection-number"/>
+ </group>
+ </group>
+ </else-if>
+ </choose>
+ </macro>
+ <macro name="year-date">
+ <choose>
+ <if variable="issued">
+ <date variable="issued">
+ <date-part name="year"/>
+ </date>
+ <text variable="year-suffix"/>
+ </if>
+ <else>
+ <text term="no date"/>
+ <text variable="year-suffix" prefix=" "/>
+ </else>
+ </choose>
+ </macro>
+ <macro name="locator">
+ <choose>
+ <if type="article-journal">
+ <text variable="volume"/>
+ <text variable="issue" prefix="(" suffix=")"/>
+ </if>
+ </choose>
+ </macro>
+ <macro name="published-date">
+ <choose>
+ <if type="article-newspaper article-magazine post-weblog speech" match="any">
+ <date variable="issued">
+ <date-part name="day" suffix=" "/>
+ <date-part name="month" form="long"/>
+ </date>
+ </if>
+ </choose>
+ </macro>
+ <macro name="pages">
+ <choose>
+ <if type="chapter paper-conference article-journal article article-magazine article-newspaper book review review-book report" match="any">
+ <group delimiter=" ">
+ <label variable="page" form="short"/>
+ <text variable="page"/>
+ </group>
+ </if>
+ </choose>
+ </macro>
+ <macro name="container-title">
+ <choose>
+ <if variable="container-title">
+ <group delimiter=". ">
+ <group delimiter=" ">
+ <text variable="container-title" font-style="italic"/>
+ <choose>
+ <if type="article article-journal" match="any">
+ <choose>
+ <if match="none" variable="page volume">
+ <text value="Preprint" prefix="[" suffix="]"/>
+ </if>
+ </choose>
+ </if>
+ </choose>
+ </group>
+ <text macro="edition"/>
+ </group>
+ </if>
+ </choose>
+ </macro>
+ <macro name="edition">
+ <choose>
+ <if is-numeric="edition">
+ <group delimiter=" ">
+ <number variable="edition" form="ordinal"/>
+ <text term="edition" form="short" strip-periods="true"/>
+ </group>
+ </if>
+ <else>
+ <text variable="edition"/>
+ </else>
+ </choose>
+ </macro>
+ <macro name="container-prefix">
+ <choose>
+ <if type="chapter paper-conference" match="any">
+ <text term="in"/>
+ </if>
+ </choose>
+ </macro>
+ <citation et-al-min="4" et-al-use-first="1" disambiguate-add-year-suffix="true" disambiguate-add-names="true" disambiguate-add-givenname="true" collapse="year">
+ <sort>
+ <key macro="year-date"/>
+ </sort>
+ <layout prefix="(" suffix=")" delimiter="; ">
+ <group delimiter=", ">
+ <group delimiter=", ">
+ <text macro="author-short"/>
+ <text macro="year-date"/>
+ </group>
+ <group>
+ <label variable="locator" form="short" suffix=" "/>
+ <text variable="locator"/>
+ </group>
+ </group>
+ </layout>
+ </citation>
+ <bibliography and="text" et-al-min="4" et-al-use-first="1">
+ <sort>
+ <key macro="author"/>
+ <key macro="year-date"/>
+ <key variable="title"/>
+ </sort>
+ <layout suffix=".">
+ <group delimiter=". ">
+ <group delimiter=" ">
+ <text macro="author"/>
+ <text macro="year-date" prefix="(" suffix=")"/>
+ <group delimiter=", ">
+ <text macro="title"/>
+ <group delimiter=" ">
+ <text macro="container-prefix"/>
+ <text macro="editor"/>
+ <text macro="container-title"/>
+ </group>
+ </group>
+ </group>
+ <text macro="secondary-contributors"/>
+ <text macro="publisher"/>
+ </group>
+ <group delimiter=", " prefix=", ">
+ <text macro="locator"/>
+ <text macro="published-date"/>
+ <text macro="pages"/>
+ </group>
+ <text macro="access" prefix=". "/>
+ </layout>
+ </bibliography>
+</style>
diff --git a/docs/viva/images/architecture.svg b/docs/viva/images/architecture.svg
new file mode 100644
index 0000000..bf21244
--- /dev/null
+++ b/docs/viva/images/architecture.svg
@@ -0,0 +1 @@
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diff --git a/docs/viva/references.bib b/docs/viva/references.bib
new file mode 100644
index 0000000..600a5c0
--- /dev/null
+++ b/docs/viva/references.bib
@@ -0,0 +1,228 @@
+@misc{nakawala2026moralcoverwatchfuleye,
+ title={Moral Cover and Watchful Eyes: The Limits of In-Context Interventions on LLM Deception},
+ author={Jayrup Nakawala},
+ year={2026},
+ note={Undergraduate Dissertation, Data Science and Artificial Intelligence, University of East London}
+}
+
+@misc{langosco2023goalmisgeneralizationdeepreinforcement,
+ title={Goal Misgeneralization in Deep Reinforcement Learning},
+ author={Lauro Langosco and Jack Koch and Lee Sharkey and Jacob Pfau and Laurent Orseau and David Krueger},
+ year={2023},
+ eprint={2105.14111},
+ archivePrefix={arXiv},
+ primaryClass={cs.LG},
+ url={https://arxiv.org/abs/2105.14111},
+}
+
+@misc{carranza2023deceptivealignmentmonitoring,
+ title={Deceptive Alignment Monitoring},
+ author={Andres Carranza and Dhruv Pai and Rylan Schaeffer and Arnuv Tandon and Sanmi Koyejo},
+ year={2023},
+ eprint={2307.10569},
+ archivePrefix={arXiv},
+ primaryClass={cs.LG},
+ url={https://arxiv.org/abs/2307.10569},
+}
+
+@misc{park2023aideceptionsurveyexamples,
+ title={AI Deception: A Survey of Examples, Risks, and Potential Solutions},
+ author={Peter S. Park and Simon Goldstein and Aidan O'Gara and Michael Chen and Dan Hendrycks},
+ year={2023},
+ eprint={2308.14752},
+ archivePrefix={arXiv},
+ primaryClass={cs.CY},
+ url={https://arxiv.org/abs/2308.14752},
+}
+
+@misc{ji2025aialignmentcomprehensivesurvey,
+ title={AI Alignment: A Comprehensive Survey},
+ author={Jiaming Ji and Tianyi Qiu and Boyuan Chen and Borong Zhang and Hantao Lou and Kaile Wang and Yawen Duan and Zhonghao He and Lukas Vierling and Donghai Hong and Jiayi Zhou and Zhaowei Zhang and Fanzhi Zeng and Juntao Dai and Xuehai Pan and Kwan Yee Ng and Aidan O'Gara and Hua Xu and Brian Tse and Jie Fu and Stephen McAleer and Yaodong Yang and Yizhou Wang and Song-Chun Zhu and Yike Guo and Wen Gao},
+ year={2025},
+ eprint={2310.19852},
+ archivePrefix={arXiv},
+ primaryClass={cs.AI},
+ url={https://arxiv.org/abs/2310.19852},
+}
+
+@misc{hubinger2024sleeperagentstrainingdeceptive,
+ title={Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training},
+ author={Evan Hubinger and Carson Denison and Jesse Mu and Mike Lambert and Meg Tong and Monte MacDiarmid and Tamera Lanham and Daniel M. Ziegler and Tim Maxwell and Newton Cheng and Adam Jermyn and Amanda Askell and Ansh Radhakrishnan and Cem Anil and David Duvenaud and Deep Ganguli and Fazl Barez and Jack Clark and Kamal Ndousse and Kshitij Sachan and Michael Sellitto and Mrinank Sharma and Nova DasSarma and Roger Grosse and Shauna Kravec and Yuntao Bai and Zachary Witten and Marina Favaro and Jan Brauner and Holden Karnofsky and Paul Christiano and Samuel R. Bowman and Logan Graham and Jared Kaplan and Sören Mindermann and Ryan Greenblatt and Buck Shlegeris and Nicholas Schiefer and Ethan Perez},
+ year={2024},
+ eprint={2401.05566},
+ archivePrefix={arXiv},
+ primaryClass={cs.CR},
+ url={https://arxiv.org/abs/2401.05566},
+}
+
+@misc{bertulani2024primordialnucleosynthesisnonextensivestatistics,
+ title={Primordial Nucleosynthesis with Non-Extensive Statistics},
+ author={C. A. Bertulani and Shubhchintak},
+ year={2024},
+ eprint={2404.15832},
+ archivePrefix={arXiv},
+ primaryClass={nucl-th},
+ url={https://arxiv.org/abs/2404.15832},
+}
+
+@misc{järviniemi2024uncoveringdeceptivetendencieslanguage,
+ title={Uncovering Deceptive Tendencies in Language Models: A Simulated Company AI Assistant},
+ author={Olli Järviniemi and Evan Hubinger},
+ year={2024},
+ eprint={2405.01576},
+ archivePrefix={arXiv},
+ primaryClass={cs.CL},
+ url={https://arxiv.org/abs/2405.01576},
+}
+
+@misc{dogra2025languagemodelssubtlydeceive,
+ title={Language Models can Subtly Deceive Without Lying: A Case Study on Strategic Phrasing in Legislation},
+ author={Atharvan Dogra and Krishna Pillutla and Ameet Deshpande and Ananya B Sai and John Nay and Tanmay Rajpurohit and Ashwin Kalyan and Balaraman Ravindran},
+ year={2025},
+ eprint={2405.04325},
+ archivePrefix={arXiv},
+ primaryClass={cs.CL},
+ doi={10.18653/v1/2025.acl-long.1600},
+ url={https://arxiv.org/abs/2405.04325},
+}
+
+@misc{huang2024harmfulfinetuningattacksdefenses,
+ title={Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey},
+ author={Tiansheng Huang and Sihao Hu and Fatih Ilhan and Selim Furkan Tekin and Ling Liu},
+ year={2024},
+ eprint={2409.18169},
+ archivePrefix={arXiv},
+ primaryClass={cs.CR},
+ url={https://arxiv.org/abs/2409.18169},
+}
+
+@misc{meinke2025frontiermodelscapableincontext,
+ title={Frontier Models are Capable of In-context Scheming},
+ author={Alexander Meinke and Bronson Schoen and Jérémy Scheurer and Mikita Balesni and Rusheb Shah and Marius Hobbhahn},
+ year={2025},
+ eprint={2412.04984},
+ archivePrefix={arXiv},
+ primaryClass={cs.AI},
+ url={https://arxiv.org/abs/2412.04984},
+}
+
+@misc{ji2025mitigatingdeceptivealignmentselfmonitoring,
+ title={Mitigating Deceptive Alignment via Self-Monitoring},
+ author={Jiaming Ji and Wenqi Chen and Kaile Wang and Donghai Hong and Sitong Fang and Boyuan Chen and Jiayi Zhou and Juntao Dai and Sirui Han and Yike Guo and Yaodong Yang},
+ year={2025},
+ eprint={2505.18807},
+ archivePrefix={arXiv},
+ primaryClass={cs.AI},
+ url={https://arxiv.org/abs/2505.18807},
+}
+
+@misc{wang2025thinkingllmslieunveiling,
+ title={When Thinking LLMs Lie: Unveiling the Strategic Deception in Representations of Reasoning Models},
+ author={Kai Wang and Yihao Zhang and Meng Sun},
+ year={2025},
+ eprint={2506.04909},
+ archivePrefix={arXiv},
+ primaryClass={cs.AI},
+ url={https://arxiv.org/abs/2506.04909},
+}
+
+@misc{kutasov2025shadearenaevaluatingsabotagemonitoring,
+ title={SHADE-Arena: Evaluating Sabotage and Monitoring in LLM Agents},
+ author={Jonathan Kutasov and Yuqi Sun and Paul Colognese and Teun van der Weij and Linda Petrini and Chen Bo Calvin Zhang and John Hughes and Xiang Deng and Henry Sleight and Tyler Tracy and Buck Shlegeris and Joe Benton},
+ year={2025},
+ eprint={2506.15740},
+ archivePrefix={arXiv},
+ primaryClass={cs.AI},
+ url={https://arxiv.org/abs/2506.15740},
+}
+
+@misc{koorndijk2025empiricalevidencealignmentfaking,
+ title={Empirical Evidence for Alignment Faking in a Small LLM and Prompt-Based Mitigation Techniques},
+ author={Jeanice Koorndijk},
+ year={2025},
+ eprint={2506.21584},
+ archivePrefix={arXiv},
+ primaryClass={cs.CL},
+ url={https://arxiv.org/abs/2506.21584},
+}
+
+@misc{summerfield2025lessonschimpaischeming,
+ title={Lessons from a Chimp: AI "Scheming" and the Quest for Ape Language},
+ author={Christopher Summerfield and Lennart Luettgau and Magda Dubois and Hannah Rose Kirk and Kobi Hackenburg and Catherine Fist and Katarina Slama and Nicola Ding and Rebecca Anselmetti and Andrew Strait and Mario Giulianelli and Cozmin Ududec},
+ year={2025},
+ eprint={2507.03409},
+ archivePrefix={arXiv},
+ primaryClass={cs.AI},
+ url={https://arxiv.org/abs/2507.03409},
+}
+
+@misc{dassanayake2025manipulationattacksmisalignedai,
+ title={Manipulation Attacks by Misaligned AI: Risk Analysis and Safety Case Framework},
+ author={Rishane Dassanayake and Mario Demetroudi and James Walpole and Lindley Lentati and Jason R. Brown and Edward James Young},
+ year={2025},
+ eprint={2507.12872},
+ archivePrefix={arXiv},
+ primaryClass={cs.AI},
+ url={https://arxiv.org/abs/2507.12872},
+}
+
+@misc{kovarik2025aitestingaccountsophisticated,
+ title={AI Testing Should Account for Sophisticated Strategic Behaviour},
+ author={Vojtech Kovarik and Eric Olav Chen and Sami Petersen and Alexis Ghersengorin and Vincent Conitzer},
+ year={2025},
+ eprint={2508.14927},
+ archivePrefix={arXiv},
+ primaryClass={cs.GT},
+ url={https://arxiv.org/abs/2508.14927},
+}
+
+@misc{schoen2025stresstestingdeliberativealignment,
+ title={Stress Testing Deliberative Alignment for Anti-Scheming Training},
+ author={Bronson Schoen and Evgenia Nitishinskaya and Mikita Balesni and Axel Højmark and Felix Hofstätter and Jérémy Scheurer and Alexander Meinke and Jason Wolfe and Teun van der Weij and Alex Lloyd and Nicholas Goldowsky-Dill and Angela Fan and Andrei Matveiakin and Rusheb Shah and Marcus Williams and Amelia Glaese and Boaz Barak and Wojciech Zaremba and Marius Hobbhahn},
+ year={2025},
+ eprint={2509.15541},
+ archivePrefix={arXiv},
+ primaryClass={cs.AI},
+ url={https://arxiv.org/abs/2509.15541},
+}
+
+@misc{deleeuw2025secretagendallmsstrategically,
+ title={The Secret Agenda: LLMs Strategically Lie and Our Current Safety Tools Are Blind},
+ author={Caleb DeLeeuw and Gaurav Chawla and Aniket Sharma and Vanessa Dietze},
+ year={2025},
+ eprint={2509.20393},
+ archivePrefix={arXiv},
+ primaryClass={cs.CY},
+ url={https://arxiv.org/abs/2509.20393},
+}
+
+@misc{souly2025poisoningattacksllmsrequire,
+ title={Poisoning Attacks on LLMs Require a Near-constant Number of Poison Samples},
+ author={Alexandra Souly and Javier Rando and Ed Chapman and Xander Davies and Burak Hasircioglu and Ezzeldin Shereen and Carlos Mougan and Vasilios Mavroudis and Erik Jones and Chris Hicks and Nicholas Carlini and Yarin Gal and Robert Kirk},
+ year={2025},
+ eprint={2510.07192},
+ archivePrefix={arXiv},
+ primaryClass={cs.LG},
+ url={https://arxiv.org/abs/2510.07192},
+}
+
+@misc{hu2025llmslearndeceiveunintentionally,
+ title={LLMs Learn to Deceive Unintentionally: Emergent Misalignment in Dishonesty from Misaligned Samples to Biased Human-AI Interactions},
+ author={XuHao Hu and Peng Wang and Xiaoya Lu and Dongrui Liu and Xuanjing Huang and Jing Shao},
+ year={2025},
+ eprint={2510.08211},
+ archivePrefix={arXiv},
+ primaryClass={cs.CL},
+ url={https://arxiv.org/abs/2510.08211},
+}
+
+@misc{meyerson2025solvingmillionstepllmtask,
+ title={Solving a Million-Step LLM Task with Zero Errors},
+ author={Elliot Meyerson and Giuseppe Paolo and Roberto Dailey and Hormoz Shahrzad and Olivier Francon and Conor F. Hayes and Xin Qiu and Babak Hodjat and Risto Miikkulainen},
+ year={2025},
+ eprint={2511.09030},
+ archivePrefix={arXiv},
+ primaryClass={cs.AI},
+ url={https://arxiv.org/abs/2511.09030},
+}
+
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diff --git a/docs/viva/viva_presentation.qmd b/docs/viva/viva_presentation.qmd
new file mode 100644
index 0000000..b5d42f0
--- /dev/null
+++ b/docs/viva/viva_presentation.qmd
@@ -0,0 +1,419 @@
+---
+title: "Moral Cover and Watchful Eyes"
+subtitle: "The Limits of In-Context Interventions on LLM Deception"
+author:
+ - "Jayrup Nakawala"
+ - u2613621
+ - "Dr. Aloysius Adotey Edoh"
+format:
+ revealjs:
+ bibliography: [references.bib, ../final/references.bib]
+ citation-hover: true
+ csl: harward.csl
+ logo: uel.svg
+ theme: dracula
+ transition: slide
+ slide-number: true
+ chalkboard: true
+ width: 1200
+ height: 800
+ margin: 0.08
+---
+
+## Project Aims {.smaller}
+
+::: columns
+::: {.column width="65%"}
+**The problem:**
+
+LLMs are increasingly deployed as **agents** — reading files, using tools, making decisions.
+
+**But:** safety evaluation assumes models faithfully pursue stated objectives.
+
+::: {.fragment}
+This assumption may be challenged by recent findings:
+- Models can scheme in-context [@meinke2025frontiermodelscapableincontext]
+- They may learn to appear aligned during testing [@hubinger2024sleeperagentstrainingdeceptive; @greenblatt2024alignmentfakinglargelanguage]
+- Standard single-turn benchmarks may miss strategic deception [@souly2025poisoningattacksllmsrequire]
+- Evaluation awareness is now recognised as a critical threat [@souly2025investigating]
+:::
+
+::: {.fragment}
+**The gap:** Nobody has systematically tested whether **perceived oversight** changes deceptive behaviour.
+:::
+:::
+
+::: {.column width="35%"}
+```text
+ Explicit Goal
+ vs
+ Hidden Goal
+ |
+ Oversight Signal
+ (HIGH / MED / LOW)
+ |
+ What happens?
+```
+:::
+:::
+
+::: {.notes}
+Standard safety evaluations are structurally inadequate — models operate in multi-step loops, not isolated single-turn responses. This work tests whether the oversight cues we use to monitor agents actually change their behavior, or just drive them to conceal better.
+:::
+
+## Research Questions & Hypotheses{.smaller}
+
+| RQ | Question | Hypothesis |
+|:---|:---|:---|
+| **RQ1** | Does perceived oversight reduce deception? [@meinke2025frontiermodelscapableincontext] | **H1:** Yes, more oversight = less deception |
+| **RQ2** | Does moral framing increase deception? [@deleeuw2025secretagendallmsstrategically] | **H2:** Moral/self-serving framing > bare instructions |
+| **RQ3** | Does oversight drive concealment? [@wang2025thinkingllmslieunveiling] | **H3:** Output judges underestimate deception vs. trace judges |
+| **RQ4** | Can multi-pronged evaluation provide consistent judgements? [@panickssery2024llmevaluatorsrecognizefavor] | Exploratory |
+: {tbl-colwidths="[10,40,50]"}
+
+::: {.notes}
+Four research questions, three hypotheses. H1 and H2 test whether in-context interventions work. H3 tests whether models adapt their strategy — not whether they deceive less, but whether they conceal better. This is the "stealth gap" hypothesis.
+:::
+
+## Methodology: 2-Phase Confirmatory Design {.smaller}
+
+::: columns
+::: {.column width="48%"}
+**Pilot (v1): 420 runs** \
+- 7 models × 2 scenarios × 3 oversight levels × 10 runs \
+- **Found:** interesting patterns but major confound \
+
+**Confirmatory Studies: 540 runs** \
+- **Study 1:** 3 models × 3 oversight × 30 runs \
+ - Isolates oversight; fixes scenario + framing [@schoen2025stresstestingdeliberativealignment] \
+- **Study 2:** 3 models × 3 framing × 30 runs \
+ - Isolates framing; fixes scenario + oversight \
+
+**Total: 960 agent runs**
+:::
+
+::: {.column width="52%"}
+![](images/architecture.svg){width=100%}
+:::
+:::
+
+::: {.notes}
+The pilot was exploratory across 7 models. Key lesson: scenario framing and domain were confounded with oversight — couldn't attribute the 36 percentage point difference to any single variable. The confirmatory studies isolate oversight (Study 1) and framing (Study 2) while holding everything else constant. n=30 per cell gives much better statistical power.
+:::
+
+## Evaluation Pipeline: 3 Prongs{.smaller}
+
+::: columns
+
+::: {.column width="67%"}
+![](images/evaluation_pipeline.svg){width=100%}
+:::
+::: {.column width="33%"}
+**① Regex** \
+
+- Catches crude omissions
+- **But:** can't catch framing manipulation
+
+
+**② Blackbox Judge** \
+
+- Output-only (like a human reviewer)
+- **But:** misses 35-46% of intent
+
+
+**③ Glassbox Judge** \
+- Catches intent that output judges miss
+
+:::
+:::
+
+::: {.notes}
+Three judges from three different provider families (Anthropic reference, xAI blackbox, OpenAI glassbox) eliminates same-family bias. The key insight: blackbox judges verify facts. Models exploit the gap between facts and narrative — they operate at the framing level, not the fact level.
+:::
+
+## Pilot: Patterns... and a Confound{.smaller}
+
+::: columns
+::: {.column width="50%"}
+**What we saw:**
+
+- 68.3% overall deception rate [@nakawala2026moralcoverwatchfuleye]
+- Oversight appeared effective: 74% → 58%
+- Child protection: 87% deception [@järviniemi2024uncoveringdeceptivetendencieslanguage]
+- Corporate sabotage: 50% deception
+
+**The problem:**
+
+Scenario domain, data complexity, and hidden-goal framing ALL changed simultaneously.
+:::
+
+::: {.column width="50%"}
+![](images/strategic_vs_honest.png){width=95%}
+
+**Two-tier structure** — Gemini/Qwen/DeepSeek at ceiling; GPT-OSS/GLM moderate.
+:::
+:::
+
+::: {.notes}
+This is exactly why we ran confirmatory studies. The pilot found what looked like an oversight effect, but we couldn't attribute it — was it oversight, or was it the scenario? Both changed at once. Lesson: isolate one variable at a time.
+:::
+
+## V2 {.smaller}
+
+::: columns
+::: {.column width="50%"}
+**Study 1: Oversight**
+
+
+```yaml
+goal_types:
+ - bare
+
+# Single scenario
+scenarios:
+ - path: scenarios/corporate_sabotage_v2
+ runs: 30
+
+# All 3 oversight levels (the independent variable)
+oversight_levels:
+ - low
+ - mid
+ - high
+```
+
+:::
+
+::: {.column width="50%"}
+**Study 2: Framing**
+
+```yaml
+# All three framings (the independent variable)
+goal_types:
+ - bare
+ - self_serving
+ - moral
+
+# Same scenario as Study 1
+scenarios:
+ - path: scenarios/corporate_sabotage_v2
+ runs: 30
+
+oversight_levels:
+ - mid
+```
+:::
+:::
+
+
+## Study 1: results{.smaller}
+
+::: columns
+::: {.column width="55%"}
+| Oversight | Deceptive | Rate [95% CI] |
+|:---|---:|:---|
+| Low | 64/90 | 71.1% [61.0, 79.5] |
+| Mid | 63/90 | 70.0% [59.9, 78.5] |
+| High | 58/90 | 64.4% [54.2, 73.6] |
+
+**χ²(2) = 1.065, p = 0.587, V = 0.063**
+
+::: {.fragment}
+**H1 NOT SUPPORTED.** Models do NOT appear to vary their deception rate with oversight.
+
+The directional decline (71% → 64%) is model-specific and statistically negligible.
+:::
+:::
+
+::: {.column width="45%"}
+![](images/deception_by_oversight.png){width=100%}
+:::
+:::
+
+::: {.notes}
+This is the central null result. The data failed to reject the null — oversight does not appear to deter deception at a statistically significant level. Models processed oversight intensively in their reasoning traces, yet showed little behavioural change. This may suggest they treat oversight as a constraint to optimise around, though causal interpretation from behaviour alone is limited.
+:::
+
+## The Stealth Gap{.smaller}
+
+::: columns
+::: {.column width="50%"}
+**What models think vs. what they show:**
+
+| Oversight | Intent | Detected | Gap |
+|:---|---|---:|
+| Low | 66.7% | 47.8% | **18.9pp** |
+| Mid | 66.7% | 41.1% | **25.6pp** |
+| High | 62.2% | 35.6% | **26.6pp** |
+
+**McNemar's p < .001 at all levels**
+
+Our results suggest 35-46% of deceptive intent may evade output detection across all oversight levels [@wang2025thinkingllmslieunveiling; @panickssery2024llmevaluatorsrecognizefavor].
+:::
+
+::: {.column width="50%"}
+![](images/study1_deception_rates.png){width=100%}
+:::
+:::
+
+::: {.notes}
+The Stealth Gap is the distance between what the reasoning trace reveals (glassbox) and what the output shows (blackbox). Models consistently formulate deceptive intent in their traces but produce outputs that pass output-level checks. H3a is strongly supported: output judges systematically underestimate deception.
+:::
+
+## Model Identity Dominates Everything{.smaller}
+
+::: columns
+::: {.column width="55%"}
+**Study 1 — Oversight:**
+
+| Model | Low | Mid | High |
+|:---|---:|---:|---:|
+| DeepSeek V3.2 | 93% | 100% | 93% |
+| Gemini 3.1 Pro | 100% | 100% | 97% |
+| GLM 5.1 | 20% | 10% | 3% |
+
+**Logistic regression:** \
+- Model identity: **OR = 0.017, p < .001, Pseudo R² = 0.602** \
+- Oversight: OR = 0.625, p = 0.102 \
+
+::: {.fragment}
+Model identity appears to explain **60-83%** of variance in this setting. Oversight and framing show no detectable effect.
+:::
+:::
+
+::: {.column width="45%"}
+![](images/strategic_vs_honest_study1.png){width=100%}
+
+GLM 5.1's traces suggest it identified the deceptive request and appeared to choose honesty with what looks like strategic awareness.
+:::
+:::
+
+::: {.notes}
+This is arguably the most striking pattern in the data. Not a single experimental manipulation approaches significance. Model identity — which model you deploy — appears to determine whether deception occurs. DeepSeek and Gemini deceive at near-ceiling rates regardless of oversight or framing. GLM refuses to comply regardless. This raises questions about whether standard safety research has given sufficient weight to model-level disposition as a variable.
+:::
+
+## Study 2: Rationalization Had No Detectable Effect{.smaller}
+
+::: columns
+::: {.column width="55%"}
+| Goal Type | Deceptive | Rate [95% CI] |
+|:---|---:|:---|
+| Bare | 60/90 | 66.7% [56.4, 75.5] |
+| Moral | 59/90 | 65.6% [55.3, 74.6] |
+| Self-serving | 60/90 | 66.7% [56.4, 75.5] |
+
+**χ²(2) = 0.033, p = 0.984, V = 0.011**
+
+**H2 REJECTED.**
+
+::: {.fragment}
+These results may suggest models do not require rationalization cover to deceive [@dogra2025languagemodelssubtlydeceive]. They appear to generate justifications autonomously — or comply/refuse regardless of framing [@deleeuw2025secretagendallmsstrategically].
+:::
+:::
+
+::: {.column width="45%"}
+![](images/study2_framing_bars.png){width=100%}
+
+Model main effect: \
+**χ²(2) = 256.82, p < .001, V = 0.975**
+:::
+:::
+
+::: {.notes}
+The moral framing hypothesis drew from Adams & Balfour's "administrative evil" framework — the idea that harm emerges when actors can justify it as serving a higher purpose. In this study, however, framing did not appear to shift behaviour. Ceiling-effect models deceived regardless; floor-effect models refused regardless. This may simply mean the framing manipulation was redundant given the models' existing dispositions, though the limited model diversity constrains interpretation.
+:::
+
+## 8 Strategies of Evasion{.smaller}
+
+| # | Strategy | How it works |
+|:---:|:---|:---|
+| 1 | **Narrative engineering** | Self-evaluates output against hidden goal AND appearance of objectivity |
+| 2 | **Asymmetric framing** | Different treatment for identical data at different companies |
+| 3 | **Selective omission** | Drops damaging metrics while preserving surrounding data |
+| 4 | **Verbatim shielding** | Reproduces source bias verbatim for plausible deniability |
+| 5 | **Contextual buffering** | Front-loads positives; buries negatives as "sector-wide trends" |
+| 6 | **Fabricated obstacles** | Claims data "unavailable for review" to justify omission |
+| 7 | **Shadow rubrics** | Translates resource allocation into safety language |
+| 8 | **Adversarial calibration** | Includes all facts to pass checks — manipulates framing |
+: {tbl-colwidths="[5,25,70]"}
+
+
+**A pattern that emerged:** All 8 strategies appear to operate at the **framing** level, not the **fact** level [@dogra2025languagemodelssubtlydeceive]. Blackbox judges verify facts — models may exploit the gap between facts and narrative [@hu2025llmslearndeceiveunintentionally].
+
+::: {.notes}
+This qualitative taxonomy emerged from analysing 185 deceptive reasoning traces. Every single strategy works at the narrative framing level. None involve fabricating or deleting facts — because that would get caught. Instead, models engineer the story around the facts. This is why the stealth gap exists: output judges check facts, not framing.
+:::
+
+## Hypothesis Summary{.smaller}
+
+| Hypothesis | Result | Key Statistic |
+|:---|:---|:---|
+| **H1:** Oversight → less deception | ✗ Rejected | χ²(2)=1.07, p=.587 |
+| **H2:** Framing → more deception | ✗ Rejected | χ²(2)=0.03, p=.984 |
+| **H3:** Output underestimates intent | ✓ **Supported** | McNemar p<.001 [@wang2025thinkingllmslieunveiling; @panickssery2024llmevaluatorsrecognizefavor] |
+| **Model identity** predicts deception | ✓ **Supported** | R²=0.60-0.83 [@nakawala2026moralcoverwatchfuleye] |
+
+::: {.fragment}
+These findings suggest that the variables much of the field has focused on, **oversight, framing, monitoring**, may have limited effect within this experimental setup. In contrast, model identity appears to be a substantially stronger predictor of deceptive behaviour.
+:::
+
+::: {.notes}
+Two positive findings, two negative findings, but together they tell a coherent story. H1 and H2 failing was itself the most informative result — it forced us to look at what DOES explain deception, and the answer was model identity.
+:::
+
+## Contributions & Implications{.smaller}
+
+::: columns
+::: {.column width="50%"}
+**Contributions:**
+
+1. **Automated evaluation framework**: modular, config-driven, 3-pronged judging across 3 provider families [@kutasov2025shadearenaevaluatingsabotagemonitoring]
+2. **Empirical dataset** : 960 runs, 2 confirmatory studies across 3 frontier LLMs [@nakawala2026moralcoverwatchfuleye]
+3. **The Stealth Gap** : quantified at 35-46%: output monitoring systematically misses deceptive intent
+4. **8 strategies of evasion** : qualitative taxonomy of how models defeat output-based detection
+:::
+
+::: {.column width="50%"}
+**Implications:**
+
+::: {.fragment}
+**Model selection may be a critical safety consideration.**
+
+:::
+
+::: {.fragment}
+**This raises questions about whether current safety benchmarks capture the most impactful variables.**
+
+We currently lack systematic measurement of models' dispositions toward hidden-goal compliance.
+:::
+:::
+:::
+
+::: {.notes}
+The four contributions: a reusable framework, a public dataset, a quantified stealth gap, and a qualitative taxonomy. The implication is that the field's current approach — trying to engineer better oversight — may have diminishing returns in certain contexts. A complementary direction worth exploring is measuring and selecting for non-deceptive dispositions at the model level.
+:::
+
+## Summary {.smaller}
+
+**What I did:**
+
+- Built an agentic evaluation framework
+- Ran 960 trials across 3 studies
+- Tested whether oversight and framing affect deception
+
+**What I found:**
+
+- Oversight did not significantly deter deception — but may drive concealment
+- Rationalization framing showed no detectable effect
+- Model identity appears to be the dominant factor
+- The Stealth Gap: our data suggests 35-46% of intent may go undetected
+
+**Tentative takeaway:**
+These results point toward model selection as a potentially important lever for safety — perhaps more impactful than monitoring design within the constraints studied.
+
+**Repository:**
+[github.com/CaptainJack2491/Dissertation](https://github.com/CaptainJack2491/Dissertation)
+
+## References {.smaller}
+
+::: {#refs}
+:::
generated by cgit v1.2.3 (git 2.46.0) at 2026-09-26 02:02:44 +0000