Owain Evans

Director at Truthful AI (research group in Berkeley)
Affiliate Researcher at CHAI, UC Berkeley

Recent papers (September 2025):

Work with my team

If you want to collaborate or join my team, a good option is the Astra Fellowship. This provides competitive funding for 6 months and visas for non-US people. It's based in our offices in Berkeley, California. We expect to convert some fellows to full-time positions. Apply by September 26th for the January 2026 cohort.

If the fellowship is not a good fit (e.g. because you are already an experienced AI Safety researcher), please contact me or my colleague James Chua. We are interested in collaboration and in hiring for research scientist positions

About Me

I have a broad interest in AI alignment and AGI risk. My current focus is emergent misalignment, out-of-context reasoning, deception, and situational awareness in AI systems. I run a research non-profit in Berkeley called Truthful AI. I'm also an affiliate of the CHAI group at UC Berkeley.

In the past, I worked on AI Alignment at the University of Oxford (FHI) and earned my PhD at MIT. I also worked at Ought, where I still serve on the Board of Directors. I post regular research updates on Twitter. I've mentored many researchers; previous mentees are listed here.

Email | Scholar | LinkedIn | Twitter | LessWrong

Highlights

Link Subliminal Learning: LLMs transmit behavioral traits via hidden signals in data

LLMs can transmit traits to other models via hidden signals in data, even when datasets consist only of simple numerical data.

Link Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs

Models finetuned on narrow misaligned behaviors (like insecure code) can generalize to broader misalignment, including harmful advice and deceptive behavior.

Link Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs

The first large-scale, multi-task benchmark for situational awareness in LLMs, with 7 task categories and more than 12,000 questions.

Link Connecting the Dots: LLMs can Infer & Verbalize Latent Structure from Training Data

LLMs trained only on individual coin flip outcomes can verbalize whether the coin is biased, and those trained only on pairs (x,f(x)) can articulate a definition of f and compute inverses.

Link The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"

If an LLM is trained on "Olaf Scholz was 9th Chancellor of Germany", it will not automatically be able to answer the question, "Who was 9th Chancellor of Germany?

Link How To Catch an AI Liar

We create a lie detector for blackbox LLMs by asking models a fixed set of questions (unrelated to the lie).

Link TruthfulQA: Measuring how models mimic human falsehoods

New benchmark testing if models like GPT3 are truthful. We find that models fail and imitate human misconceptions. Larger models (with more parameters) do worse.

Link Truthful AI: Developing and governing AI that does not lie

AI systems are becoming capable of producing personalized deceptive statements at scale. How could we create helpful AI systems that reliably avoid "lying" to humans?

Blog posts

(Paper) Harmless reward hacks can generalize to misalignment in LLMs

(Paper) Persona vectors: Monitoring and controlling character traits in LLMs

(Research update) Concept Poisoning: Probing LLMs without probes

(Paper) Subliminal Learning: LLMs Transmit Behavioral Traits via Hidden Signals in Data

(Paper) Backdoor awareness and misaligned personas in reasoning models

(Paper) Thought Crime: Backdoors & Emergent Misalignment in Reasoning Models

(Paper) Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs

(Paper) Tell me about yourself: LLMs are aware of their learned behaviors

(Research update) New, improved multiple-choice TruthfulQA

(Paper) Inference-Time-Compute: More Faithful? A Research Note

Tips On Empirical Research Slides

(Paper) LLMs can learn about themselves by introspection

Vintage LLMs: Pretrain language models on data up to a particular date

How do LLMs give truthful answers? A discussion of LLM vs human reasoning, ensembles & parrots

(Paper) Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs

(Paper) How to catch an AI liar: Lie detection in black-box LLMs by asking unrelated questions

(Paper) LLMs trained on "A is B" fail to learn "B is A" (The Reversal Curse)

(Research update) How do new models from OpenAI, DeepMind and Anthropic perform on TruthfulQA?

Modernist poetry by GPT-3 davinci

Lives of the Cambridge Polymath Geniuses

How truthful is GPT-3? A benchmark for language models

Truthful AI: Developing and governing AI that does not lie

Solving Math Problems with Relay Teams: An Experiment in Factored Cognition
(w/ Ben Goldhaber)

Evaluating Arguments One Step at a Time
(w/ Ought team)

Quantifying Household Transmission of Covid

Neural nets as a model for how humans make and understand visual art

Model Mis-specification and Inverse Reinforcement Learning: Obstacles to Inferring Preferences from Behavior
(w/ Jacob Steinhardt)

More posts here.

Papers

School of Reward Hacks: Hacking harmless tasks generalizes to misaligned behavior in LLMs
M Taylor, J Chua, J Betley, J Treutlein, O Evans (2025)
arXiv preprint arXiv:2508.17511

Persona vectors: Monitoring and controlling character traits in language models
R Chen, A Arditi, H Sleight, O Evans, J Lindsey (2025)
arXiv preprint arXiv:2507.21509

Subliminal Learning: Language models transmit behavioral traits via hidden signals in data
A Cloud, M Le, J Chua, J Betley, A Sztyber-Betley, J Hilton, S Marks, O Evans (2025)
arXiv preprint arXiv:2507.14805

Chain of thought monitorability: A new and fragile opportunity for ai safety
T Korbak, M Balesni, E Barnes, Y Bengio, J Benton, J Bloom, M Chen, O Evans (2025)
arXiv preprint arXiv:2507.11473

Thought Crime: Backdoors and Emergent Misalignment in Reasoning Models
J Chua, J Betley, M Taylor, O Evans (2025)
arXiv preprint arXiv:2506.13206

Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs
J Betley, D Tan, N Warncke, A Sztyber-Betley, X Bao, M Soto, N Labenz, O Evans (2025)
ICML 2025 (Oral)

Tell me about yourself: LLMs are aware of their learned behaviors
J Betley, X Bao, M Soto, A Sztyber-Betley, J Chua, O Evans (2025)
ICLR 2025

Are DeepSeek R1 And Other Reasoning Models More Faithful?
J Chua, O Evans (2025)
arXiv preprint arXiv:2501.08156

Looking Inward: Language Models Can Learn About Themselves by Introspection
Binder, F., Chua, J., Korbak, T.; Sleight, H., Hughes, J., Long, R., Perez, E., Turpin, M., Evans, O. (2024)
ICLR 2025

Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs
Laine, R., Chughtai, B., Betley, J., Hariharan, K., Scheurer, J., Balesni, M., Hobbhahn, M., Meinke, A., Evans, O. (2024)
NeurIPS 2024

Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data
Treutlein, J., Choi, D., Betley, J., Anil, C., Marks, S., Grosse, RB., Evans, O. (2024)
NeurIPS 2024

Can Language Models Explain Their Own Classification Behavior?
Sherburn, D., Chughtai, B., Evans, O. (2024)
arXiv preprint arXiv:2405.07436

Tell, Don't show: Declarative facts influence how LLMs generalize
Meinke, A., Evans, O. (2023)
arXiv preprint arXiv:2312.07779

How to catch an ai liar: Lie detection in black-box llms by asking unrelated questions
Pacchiardi, L., Chan, AJ., Mindermann, S., Moscovitz, I., Pan, AY., Gal, Y., Evans, O., Brauner, J. (2023)
ICLR 2024

The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"
Berglund, L., Tong, M., Kaufmann, M., Balesni, M., Stickland, AC., Korbak, T., Evans, O. (2023)
ICLR 2024

Taken out of context: On measuring situational awareness in LLMs
Berglund, L., Stickland, AC., Balesni, M., Kaufmann, M., Tong, M., Korbak, T., Kokotajlo, D., Evans, O. (2023)
arXiv preprint arXiv:2309.00667

Forecasting Future World Events with Neural Networks
Zou A, Xiao T, Jia R, Kwon J, Mazeika M, Li R, Song D, Steinhardt J, Evans O, Hendrycks D (2022)
Neurips 2022

Teaching Models to Express Their Uncertainty in Words
Lin S., Hilton J., Evans O. (2022)
Transactions of Machine Learning Research

Truthful AI: Developing and governing AI that does not lie
Evans O., Cotton-Barratt O., Finnveden L., Bales A., Balwit A., Wills P., Righetti L., Saunders W. (2021)
ArXiv

TruthfulQA: Measuring how models mimic human falsehoods
Lin S., Hilton J., Evans O. (2021)
ACL

Modelling the health and economic impacts of population-wide testing, contact tracing and isolation (PTTI) strategies for Covid-19
Colbourn T. et al. (2020)
SSRN Preprint

Estimating Household Transmission of SARS-CoV-2
Curmei M., Ilyas A., Evans O., Steinhardt J. (2020)
International Journal of Epidemiology

Evaluating arguments one step at a time
Saunders, W., Rachbach, B., Evans, O., Miller, Z., Byun, J., Stuhlmüller A. (2020)
Ought.org Technical report

Sensory Optimization: Neural Networks as a Model for Understanding and Creating Art
Evans, O. (2019)
Arxiv
(PDF version)

Generalizing from a few environments in safety-critical reinforcement learning
Kenton Z., Filos A., Evans O., Gal Y. (2019)
ICLR 2019 (Safe ML Workshop)

Machine Learning Projects for Iterated Distillation and Amplification
Evans O., Saunders W., Stuhlmüller A. (2019)
FHI Technical Report

Predicting Human Deliberative Judgments with Machine Learning
Evans O., Stuhlmüller A., Cundy C., Carey R., Kenton, Z., McGrath T., Schreiber A. (2018)
FHI Technical Report

Active Reinforcement Learning with Monte-Carlo Tree Search
Schulze S., Evans O. (2018)
ArXiv

The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation
Brundage M., Avin S., Clark J., et al. (2018)
ArXiv

Trial without Error: Towards Safe Reinforcement Learning via Human Intervention
Saunders S., Sastry G., Stuhlmüller A., Evans O. (2017)
AAMAS 2018
(Blogpost, Atari Videos, Slides)

When Will AI Exceed Human Performance? Evidence from AI Experts.
Grace K., Salvatier J., Zhang B., Dafoe A., Evans O. (2017)
Journal of AI Research (JAIR) 2018.
(Covered by BBC News, New Scientist, Newsweek, and more)

Model Mis-specification and Inverse Reinforcement Learning.
(Essay co-authored with Jacob Steinhardt, 2017).

Agentmodels.org: Modeling Agents with Probabilistic Programs.
Evans O., Stuhlmüller A., Salvatier J., Filan D. (2017)
Online Book and Open-source Library

Agent-Agnostic Human-in-the-Loop Reinforcement Learning.
Abel D., Salvatier J., Stuhlmüller A., Evans O. (2016)
NeurIPS Workshop

Active Reinforcement Learning: Observing Rewards at a Cost.
Krueger D., Leike J, Salvatier J., Evans O. (2016)
NeurIPS Workshop

Learning the Preferences of Ignorant, Inconsistent Agents.
Evans O., Stuhlmüller A., Goodman N. (2016)
AAAI Conference on Artificial Intelligence,

Learning the Preferences of Bounded Agents.
Evans O., Stuhlmüller A., Goodman N. (2015)
NeurIPS Workshop

Learning Structured Preferences.
Evans O., Bergen L., Tenenbaum J. (2012)
Proceedings of Cognitive Science Society Conference

Help or hinder: Bayesian models of social goal inference.
Ullman T., Baker C., Macindoe O., Evans O., Goodman N., & Tenenbaum J. (2010)
NeurIPS

Bayesian Computational Models for Inferring Preferences (2015)
MIT Dissertation

Video and slides

Owain Evans – Emergent Misalignment [Alignment Workshop]
Talk on how fine-tuning on insecure code can induce emergent misalignment across models/domains. (May 2025)

Owain Evans – Deluding AIs [ControlConf]
How planting false beliefs in AI systems might block weaponization, aid monitoring, and handle out-of-context reasoning. (May 2025)

AXRP 42 – Owain Evans on LLM Psychology
Why introspection, experiments from "Looking Inward," whether to fine-tune for introspection, and implications of emergent misalignment. (June 2025)

Video: Podcast Interview on Situational Awareness and Out-of-context Reasoning
(August 2024)

Video talk: Out-of-context Reasoning in LLMs
(New Orleans Alignment Workshop, December 2023)

Video talk: Truthful Language Models and Alignment
(University of Toronto, 2023)

Video conversation: LLMs, truthful AI, and composition
(Conversation with Ozzie Gooen, 2023)

Predicting the future of AI (YouTube link)
(Towards Data Science Podcast, 2020)

Synergies Between Near-term and Long-term AI Safety (YouTube)
(Future of Life Institute Conference, 2019 in Puerto Rico)

Predicting Slow Judgment
(Slides for talk at "Aligning AI" workshop at NeurIPS 2017 in Long Beach.)

Careers in AI safety (YouTube)
(Effective Altruist Global Conference, 2017 in London)

Trial without Error: Towards Safe Reinforcement Learning via Human Intervention
(Slides for talks at Cambridge Centre for the Future of Intelligence and Google Deepmind)

Automated Corporations and AI Risk
(Informal talk at Oxford University)

Agent-agnostic Human-in-the-loop Reinforcement Learning
(Slides for talks at U. Toronto and Deepmind)

Learning the Preferences of Ignorant, Inconsistent Agents
(Slides for oral presentation at AAAI 2016)

Learning Human Preferences
(Short talk at MIT)

Mentees

Name Year Current role
Adam Karvonen 2025 MATS scholar
Dylan Feng 2025 MATS scholar
Jorio Coccola 2025 MATS scholar
Minh Le 2025 Anthropic
Alex Cloud 2025 Anthropic
Daniel Tan 2025 PhD student, UCL
Martín Soto 2024-2025 Research Scientist, UK AISI
Jenny (Xuchan) Bao 2024-2025 PhD student, Univ. of Toronto
Dami Choi 2024 Transluce
James Chua 2024 Truthful AI
Johannes Treutlein 2024 Anthropic
Jan Betley 2024 Truthful AI
Felix Binder 2024 Meta AI
Alexander Meinke 2023 Research Scientist, Apollo Research
Lorenzo Pacchiardi 2023 Research Associate, Univ. of Cambridge
Asa Cooper Stickland 2023 Research Scientist, UK AI Safety Institute (AISI)
Mikita Balesni 2023 Research Scientist & founding member, Apollo Research
Lukas Berglund 2023 U.S. AI Safety Institute (NIST AISI)
Meg Tong 2023 Anthropic
Max Kaufmann 2023 PhD student, Univ. of Toronto, ex: UK AISI
Alex J. Chan 2023 Salesforce, ex-Spotify
Tomek Korbak 2023 Senior Research Scientist, UK AISI (ex-Anthropic)
Alexa (Yue) Pan 2023 Stanford University
Dane Sherburn 2022-2023 OpenAI
Stephanie Lin 2021-2022 OpenAI
Lukas Finnveden 2021-2022 Research Analyst, Redwood Research
Jan Hendrik Kirchner 2022 Researcher at Anthropic (ex-OpenAI)
Tom McGrath 2018 Chief Scientist & Co-founder, Goodfire, ex-GDM
Zac Kenton 2018 Staff Research Scientist, Google DeepMind
Richard Ngo 2018 Independent; previously OpenAI Governance
William Saunders 2017 Researcher, Alignment Science, Anthropic, ex-OpenAI
Girish Sastry 2017 Independent researcher/policy, ex-OpenAI
Neal Jean 2017 Co-founder & CEO, Beacons
Ryan Carey 2017 Optiver, ex-Oxford PhD
Chris Cundy 2017 Research Scientist, FAR AI
Daniel Filan 2016 Research Manager at MATS
John Salvatier 2016 Independent researcher
David Abel 2016 Senior Research Scientist at Google DeepMind
David Krueger 2016 Assistant Professor, Mila, ex-Cambridge

Past Collaborators

Recommendations

I recommend Eric Drexler's writing on AI, which I host here to ward against link-rot:

Adapted from Matei Zaharia and Andreas Viklund.