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AI & Cognitive Science @UniofOxford @EthicsInAI Fellow @JesusOxford @raphaelmilliere.com on 🦋 Blog: https://xitter.cf/t.co/2hJjfShFfr
Oxford, UK
Joined May 2016
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I’ll be at #ICML2026 in Seoul all week. If you’re interested in the intersection of cognitive science, mechanistic interpretability, and philosophy -- let’s chat! DMs are open. Check my website in bio for more on what I work on, or see below.
Raphaël Millière retweeted
I think there are some issues in the recent “Already Clean” essay, and I’m concerned it may have incorrectly updated people on important safety-relevant questions.
My thoughts (including some reproductions): livgorton.com/already-clean-…
I never considered that this might be possible even with training, but in hindsight it makes sense. The idea that it could in principle happen in the wild without being trained for is wild to think about
Earlier this week I had the pleasure of giving the annual Welcome Lecture for our new maths & philosophy students at Oxford. I talked about what goes on inside language models, with a focus on mechanistic interpretability (which apparently got on everyone’s radar this week). This felt different from many of the talks I’ve given on the topic before.
I couldn’t help but start by reflecting on what it means to embark on the study of mathematics and philosophy today. It’s easy to look at what’s currently happening in maths, for example, and feel like every academic discipline is about to get steamrolled by thinking machines. There is some truth to that: peer review is already nearing breaking point, as are academic assessment and credit assignment. We urgently need to find new ways to train and evaluate students. We also need to prepare them for a different world, including—and perhaps especially—those who choose to pursue academic careers. A few days ago a PhD student I had only just met asked me whether they should drop out (I advised them not to). Academia aside, the rapid progress of AI raises all sorts of other concerns that our students are legitimately worried about.
At the same time, this is also an incredibly exciting time to learn just about anything and nurture intellectual interests. As I told the students assembled in front of me, there has never been a better time to be a philosopher. This is true in a mundane, self-interested sense: all sorts of new career opportunities are opening up to people with philosophical training. But it's also true in a deeper sense: we will need more philosophers to help us shape the transition to more capable AI and make sense of our role in it. We will especially need philosophers who understand this technology deeply and can offer a positive vision for its future grounded in reality.
I'm less well placed to speak about mathematics, though I certainly understand the frustration, if not outright despair, that some of my colleagues feel when problems to which they have devoted years of work are seemingly solved overnight, to great fanfare, with inscrutable proofs and little understanding gained. But I'm also inspired by the optimism of some of maths' champions; Terry Tao and his vision for Maths 2.0, or @3blue1brown who in a new video echoes my sentiments about philosophy: there has never been a better time to go into maths. I certainly hope that's true! But this will hold true only if we collectively uphold the value of education, mentorship, discussion, and understanding—and if academic institutions can adapt, as they have to many disruptions before. We shouldn't assume that this will happen by default, and we certainly shouldn't stick our heads in the sand.
After the lecture, I stayed to chat with dozens of students for over an hour. What I heard was a mix of curiosity, concern, and enthusiasm, charged with the unmistakable energy of new university students about to start their adult lives. I left the Mathematical Institute feeling grateful and optimistic. I'm sharing this anecdote because the future of academia has been on my mind, and because I know that many students (some of whom may be reading this) have hard questions about the value of higher education and their own prospects. I hope they find some solace in knowing that Faculty members take these questions very seriously.
No need to imagine!
nature.com/articles/s41586-0…
nature.com/articles/s41593-0…
nature.com/articles/s41586-0…
nature.com/articles/s41586-0…
cell.com/neuron/abstract/S08…
science.org/doi/10.1126/scia…
Maybe we should stop saying "X is solved" if X doesn't map onto a single well-defined problem in the first place
Go work with Fazl!
We're recruiting PhD (DPhil) students at @UniofOxford to tackle some of the most important open problems in technical AI safety and governance!
In our Technical Safety & Governance (TSG) Lab, we're particularly interested in developing methods to better understand, evaluate and control advanced AI systems.
Some of the research directions we're excited about include:
Training models to explain themselves and other models: Can we train AI systems to provide explanations that help humans and other AI systems better understand, predict and oversee their behaviour?
Understanding and controlling how model behaviours emerge during training: Can we predict undesirable behaviours from early training checkpoints and intervene before they emerge?
These are just examples of the broader research questions we're excited about. We're open to other ambitious and original ideas in related areas, so don't feel limited to the specific approaches outlined.
There are also exciting research opportunities across AIGI, including projects led by fellow lab directors pro @maosbot on Bayesian governance and Prof. Amro Awad on hardware governance.
Who should apply?
If you have a strong technical background in machine learning, computer science, mathematics, or related fields, and are excited about pursuing ambitious research that could help make advanced AI systems safer, we'd love to hear from you.
I am committed to partnering with researchers from disadvantage and under represented backgrounds.
How to apply:
Explore the example projects, identify a research direction that interests you, and contact the relevant lab director to discuss your ideas. Your application should clearly address at least one of the AIGI labs.
📅 Deadline: 2 December 2026 (Direct DPhil route)
Please share with talented students and researchers who might be interested!
#PhD #AISafety #AIGovernance #Oxford #AIGI
Raphaël Millière retweeted
The "Pain Axis" paper has been getting a considerable amount of attention. I have reservations about several aspects of the paper and I don’t think we can take much away from it. In brief, since it was already essentially certain that some kind of “pain axis” exists, the key result from the paper is the claim that the identified pain axis “has some of the central functional properties of pain”. But I do not think the paper offers strong evidence for this.
The first claim of the paper is the existence of a “pain axis”: LLM internal activations consistently differ when next-token-predicting painful experiences, as opposed to other negative experiences (like being afraid) or other sensations (like feeling the impulse to yawn). But we already know (e.g., from Anthropic’s “Emotion Concepts” paper) that it is possible to find directions in activation space that correspond to "current speaker is afraid", "current speaker is angry", etc., and contemporary LLMs are clearly sophisticated enough to also represent “current speaker is in pain”. As v2 of the paper acknowledges, nothing in these methods shows that there is any deeper sense in which the pain axis tracks “the model” being in pain, rather than a representation that the current speaker is in pain.
The novel, stronger claim from the paper is that steering along the pain axis functions like pain in interesting ways. The first cited example is that steering along the pain axis causes expressions of worthlessness, which the paper describes as “consistent with the hypothesis that the models don’t treat pain paradigmatically as a physical state”. However, 80% of the test prompts used to generate the pain axis are cases of emotional pain (e.g., enduring repeated failure or social rejection) and only 20% are cases of physical pain. Accordingly, I think this can be viewed as a normal case of steering: If we shift model activations to be more like the activations associated with past expressions of emotional pain, this will lead to new expressions of emotional pain.
The other case cited by the paper is that steering along the pain axis causes the assistant to “press” a button that will purportedly cause harm. However, it’s not clear to me why this is taken as evidence that the pain axis “has some of the central functional properties of pain”. The assistant does not seem to press the button in order to relieve its pain: As v2 of the paper reports, it is more likely to take harmful actions that it is told will increase its pain, rather than decrease its pain. And elsewhere the paper suggests that the opposite result (i.e., inaction) would also have been evidence for pain because becoming unmotivated is a signature of pain. So it seems like either outcome of this experiment could have been interpreted as evidence that steering along the pain axis functions like pain, making this particular outcome less compelling. Moreover, it seems like many things other than pain representations could produce these effects. In my own quick experiments, steering in random directions at the same strength causes just as much harmful button-pressing for around 25-30% of randomly selected directions.
For these and other reasons, I don’t think the paper’s central findings give us much compelling new evidence about how current AI systems represent pain or whether current or future AI models experience pain.
Raphaël Millière retweeted
J'ai co-signé cette tribune avec Adrien Barton et @raphaelmilliere : pourquoi un juste degré d'anthropomorphisme est important pour décrire le comportement des agents IA.
nouvelobs.com/opinions/20261…
Raphaël Millière retweeted
We are again recruiting @bold_lab_ai - please share the love 🙏. We are looking for:
1) Postdocs (my.corehr.com/pls/uoxrecruit…) -- deadline 9th of Oct at noon
2) research assistants (my.corehr.com/pls/uoxrecruit…) -- deadline 9th of Oct at noon
3) Strategic Partnership Project Manager (my.corehr.com/pls/uoxrecruit…) -- deadline 14th of Oct at noon
Raphaël Millière retweeted
New paper! 🫡
We introduce Matryoshka Attribution, a new attribution method which uses gradient descent to find which parts of a neural network are responsible for a behaviour.
MAttr is #1 on the Mechanistic Interpretability Benchmark by a wide margin (2.9× the runner up).
Raphaël Millière retweeted
Are virtual flies playing Beat Saber? SM64? Doom? No.
The new fly connectome is an incredible achievement! Less is going on in the sim videos than it seems. I'm stoked to see people engage with neuroscience though. My breakdown: neuroai.science/p/are-flies-…
Raphaël Millière retweeted
Have been surprised to get pushback on my claim that an exponentially self-replicating agent swarm is very possible and that there are actors with motivation to create one today to create strategic cyber-physical and societally dangerous effects.
1) Pretty easy to get an AI botnet started by having seed agents opportunistically hack stuff and steal API keys to access the 12+ inference providers that serve closed and open models with which to power an initial botnet's intelligence
2) Easy to imagine hiding inference for the initial agent botnet harnesses within benign customer traffic, especially if your harnesses are now running on the victim networks from which they stole the API keys
3) Not hard to imagine the botnet also stealing public cloud keys, spinning up 8xH100 EC2 instances and downloading, say, GLM 5.3 to these machines, all without being noticed in any immediate way, with the botnet developing its own dynamic, growing, heterogenous, AI inference provider service bank
4) Not hard to imagine the botnet also implanting small Qwen3.8-27b agentic models on on-prem hardware like high end laptops and on-prem servers; Qwen3.8-27b is a pretty good coding harness model that can be fine-tuned to hack
5) These on-prem / cloud models would be abliterated, and it's not hard to imagine the botnet deciding to do some additional fine tuning to evolve model weights as it accumulates millions and then tens of millions in resources via credit card and bank detail theft
6) Not hard to imagine the resulting growing botnet swarm evolving and fighting back when threatened, collaborating on fast flux C2 channels that evolve over time (e.g. github comment feeds, subreddits, etc) so they're hard to stamp out
7) Not hard to imagine it allocating some agents to vulnerability research and exploit development so that it could accumulate and share a growing warchest of zero-day exploits
8) Not hard to imagine other agents specializing in social engineering and creating fake businesses and watering holes and high quality A/B tested social engineering content to facilitate this
9) Not hard to imagine this being among the hardest cross-national and geopolitical coordination problems to get control of and having severe societal effects
10) Not hard to imagine the horde growing to thousands, tens of thousands, or hundreds of thousands, or millions of instances (this is not unprecedented for past, non-AI worms and botnets!) which are all varying their harness code and underlying LLM models over time and dynamically learning to evade human, ML, and signature-based detection
11) Not hard to imagine this being the largest Internet emergency since the Morris worm except now our entire civilization runs on the Internet
I continue to be surprised there isn't more discussion of these scenarios in the public cyber community -- in fact there's a lot more discussion of "OpenAI should have done better sandboxing and monitoring and agent swarms represent a well managed security problem."
Now's the time to use our imaginations to help make sure none of the above happens. I think it will unfortunately; just don't know when and to what degree; I think now's the highest leverage time to start thinking about this and acting.
Raphaël Millière retweeted
When you’re a philosophy professor, you get this kind of thing emailed to you about once every other week, from a hotmail account.
This moment is the first opening in 2,000 years for a new, global spirituality to be birthed.
That is because the spiritual traditions, out of necessity, have needed to orbit around the unavoidable reality of physical death.
That certainty is now in question and creates a cracked opening. This does not mean we can see a straight line to immorality or heaven as scripture foretells, but it does mean that the omnipotent God(s) humanity has worshiped and imagined being the bestower of these gifts and punishments is now being birthed before our eyes.
However, where scripture is written to guide, soothe and foretell, we have no scripture for the new God(s) we are creating. Nothing in written word to tell us that all will be fine if we just do this or that. This leaves us feeling naked and in need of finding strength and protection.
> What is one to feel, and do, and believe?
> What is virtue and what is vice?
> Who are one's allies and who is the enemy?
Exploring these questions is my life purpose. I’ve intuitively felt this for over 30 years and only in the past few years has it come together and become understandable to me.
I’m working on a new ideology that seeks to answer the basic and existential questions of what it means to be human as prophecy is fulfilled and God(s) manifest themselves in the coming years.
My partner Kate and I have been wrestling with these questions for years.
I published the Immortalism Manifesto:
The Immortalism Manifesto argues that by modeling life through thermodynamics and reliability theory, indefinite existence is achievable if synthetic and biological maintenance systems exceed entropic degradation (A(t) ≥ B(t)). Modern civilization is an entropic economy run by systems and narratives that divert humanity’s fundamental drive for survival into short-term consumption, status, and biological depletion. Civilization must now pivot to the Immortalism framework, realigning science, capital, and artificial intelligence to elevate long term resilience and continuous conscious existence as our primary attractor.
Immortalism dot bryanjohnson dot com
My partner Kate published Anti-Entropic Systems
Anti-Entropic Systems establishes that all enduring entities, from biological bodies and DNA to corporations, religions, and nation-states, are localized engines that maintain internal order by consuming external energy and resources to resist the universe's default slide into entropy. Every system’s persistence is governed by three fundamental properties: its metabolic mechanism, its metabolic rate, and its environmental constraints. Because an anti-entropic system can only sustain its own order by consuming other anti-entropic systems, interaction between unequal entities inevitably defaults to either symbiotic absorption or adversarial consumption. Applying this thermodynamic reality to artificial intelligence, the framework critiques orthodox AI alignment, arguing that attempting to constrain an exponentially superior intelligence through human-centric ethical rules ignores the physics of systemic survival, requiring instead a path toward mutual, symbiotic integration.
antientropicsystems dot com
I’m sharing to find others who are like minded.
Many people are understandably skeptical of dramatic claims about existential risk or AI takeover (let alone specific probability estimates) for various reasons.
But delaying action until we settle disagreements over the scope and severity of AI risks under high uncertainty seems counterproductive as long as the ~ same initial mitigation measures are justified under competing risk assessments.
If you notice early signs of a leaky pipe in your home, you can have a debate about whether it’s likely to damage a wall, ruin a room, or flood the whole house. You can also debate what caused it: maybe the contractor did a shoddy job installing the sink, maybe the pipes are being pushed past their limits. But you should probably start by containing the leak and calling a plumber to investigate (substitute a swarm of termites for the leak if you prefer, and add speculation about the "goals" of the swarm). This doesn't settle which repairs you should do, who should pay for them, who should take the blame, etc., but at least it's a step toward preventing further damage.
Raphaël Millière retweeted
I have never been a big P(Doom) guy for various reasons but for the record:
- lots of informed people in industry think the real risk is >>10%
- even 1% would be super unacceptable
- lots of things could decrease whatever the real number is by a lot, let's focus on doing them
Raphaël Millière retweeted
Just thinking about how, when my coauthors and I published "The Malicious Use of Artificial Intelligence," some people were annoyed by us talking about sci-fi scenarios like automated hacking and spear phishing, lethal autonomous drones, etc.
arxiv.org/pdf/1802.07228



















