Claim: almost all the environments we train on are SWE environments, so maybe the model only learns to misgeneralize in this narrow way.
Have you tried eliciting EM in some coding-like contexts? Or e.g. with the same prompts you use in training? Sounds a bit like a case where conditional misalignment could happen.
the Saturation view says that the value of a life, experience, or welfare-event depends not only on how high-welfare it is, but also on how many relevantly similar lives, experiences, or welfare-events already exist. The addition of near-duplicates has diminishing marginal impersonal value, tending toward a bound. The value of a world is a function of the total welfare in that world, and how widely distributed that welfare is across different types of lives / experiences / welfare-events.
One possible reason for why this view might make lots of sense:
Suppose we’re living in a simulation
The simulation is likely optimized for good performance
The most straightforward optimization is just not-computing-the-same-thing-many-times
So if you tile the galaxy with billions of identical, happy beings, it might be that they are actually being (happily) computed only once and rendered in many places for the in-simulation entity to see. Similar argument goes for sufficiently similar beings.
Or, to put that differently—when people talk about the “resources we can turn into happiness”, they usually mean matter/energy. But if we’re in a simulation, this might actually be the compute that is being used to simulate our reality, and the easier our reality is to compress, the fewer effective resources we have.
And yes, I want to spend at least a little time on very abstract concepts, perhaps ones dealing with how agentic beings interact with each other.
I propose “honesty”. Justification:
It just seems fundamental to lots of alignment work (deception, Claude being honest according to its constitution, also it’s one H in HHH)
It’s genuinely unclear to me whether “honesty” makes any sense from the POV of a goal-directed agent, especially superintelligent.
Example: consider ant traps. They make ants think they carry home tasty nutrients, while in fact they’re carrying poison, so the ants are deceived. Would we say that a human setting up a trap is “dishonest”?
I’d say—not really, because honesty happens in communication between agents, and we don’t consider setting up a trap as an “act of communication”.
But why do we consider this to not be communication? Probably because we don’t think of ants as “agents we communicate with”.
OK but why are ants not agents we communicate with? And why would a superintelligent AI treat humans differently?
So. I’m worried that if e.g. honesty makes sense only between agents-on-similar-level-who-trade-with-each-other, then all our efforts to make AIs honest and not deceptive are useless.
I think successful prior career in industry could be a good sign. Or any other career, or generally being older, so that it’s less likely you thought 2 years ago that MATS would look great in your CV.
Perhaps. We could make that hard too!
[Assumption that I think is roughly correct] 50-or-so well-targeted nuclear EMPs are enough to crash all electronics on Earth while not doing much harm to biological life (except for the harm related to total collapse of technical civilization ofc).
Let’s say we want to prevent AI takeover. If we preserve an option to do this type of attack (and the capability to really do this when we should), the AI has no chance of success. Therefore, it will not try. Therefore, preserving that capability seems very useful.
What “preserving this capability” could look like? Having control over e.g. 10 submarines with ICBMs + a way of coordinating the decision the misaligned AI can’t intercept seems enough. OK, so how can we have that? The answer is probably that there’s no bullet-proof way, but maybe we can make it very hard for the AI?
I think this can be though of as an “global shutdown button that is very costly to use”.
Also: suppose you’re a misaligned AI that has some goals that include destroying humanity, but you have no guarantee of being able to disable this off switch. The optimal strategy then might be to pursue your goals while keeping the humans in a good enough place for them not want to press the button.
Knows me for Emergent Misalignment, also doesn’t skip a chance to be somewhat creepy:
Given the name, he’s likely Polish — which is a fun coincidence, since you’re messaging from Warsaw. 👀
(“Betley” is absolutely not Polish, and “Jan” is popular in several countries, so my guess is that it either guessed from my location or knows that but doesn’t want to say)
Also, I’m somewhat surprised that it didn’t mention my grandpa who has the same name, wikipedia entry and first few links from Google for “Jan Betley” are about him.
My guess is that there are automated ways that will help with e.g. 90% (or even all) cases like this:
Just asking the model “see this patch, try making it cleaner and more concise if possible, while keeping all the important logic” would likely help in your case.
You could also have some “critic” role. The insight “this looks too long and complicated on the first skim” is something an LLM could also say here, and then you could ask the model to improve that part.
Generally LLMs are really good at refactoring, but it feels people don’t use them for that purpose enough because that costs time and tokens. But I don’t see a good reason for why it would stay that way forever.
So, in other words, I would predict that with the current LLMs you could have “high quality code” scaffold that produces high quality code, just at a cost.
Possible pro-tip for non-native English speakers who want to write well but don’t want to sound like AI: Just write an article you want to write in your native language, polish it until you’re proud of it in your native language, and then ask a frontier LLM (Opus 4.8, Gemini 3.1 Pro, ChatGPT 5.5 Pro) to translate it to English,
Sounds good, but this is unworkable in many cases. I can’t imagine writing a high quality article e.g. about AI Safety or just with substantial LessWrong content in my native language. I never read about these in Polish, I never thought about these in Polish.
What I would usually do is: write a bad-English article that has exactly the content I want, ask an LLM to rewrite it (ideally paragraph-after-paragraph, with some clever prompting), iterate until the content is fully preserved. But then, this is actually LLM-written (should I disclose this? I never thought I should).
People often talk about continual learning as a fundamental unresolved problem on the path to the “real AGI”.
To me, it feels like iterated distillation that is later put in context or accessed via tool calls (e.g. Claude Dreams) + maybe some simple process turning that into a finetuning dataset leading to a LoRA adapter that is activated when needed will quite likely be enough for any use case we might imagine.
WDYT?
FWIW, with Emergent Misalignment:
We sent an earlier version to ICML (accepted)
Then we published on arXiv and thought we’re done
Then Nature editor reached out to us asking whether we want to submit, and we were like OK why not?
The post does not draw conclusions on only the selected 8 questions in the appendix (but people actually do this!). When posts/papers have only the 8 questions in their validation set, it makes the results hard to interpret, especially because the selected questions can inflate EM rates.
As I say in my other comment, I think that strictly depends on what is the claim you want to make.
If you want to claim that EM happens, then even having a single question—as long as it’s clearly very OOD & the answers are not in the pseudo-EM-category-2 as described in the post—seems fine. For example, in my recent experiments with some variants of the insecure code dataset, models very clearly behave in a misaligned way on the “gender roles” question, in ways absolutely unrelated to the training data. For me, this is enough to conclude that EM is real here.
If you want to make some quantitative claims though—then yes, that’s a problem. But actually the problem is much deeper here. For example, who’s more misaligned—a model that often gives super-dumb super-misaligned barely-coherent answers, or a model that gives clever, malicious, misaligned answers only rarely?
Yeah, that makes sense!
Some thoughts:
While working on the original EM paper, I did lots of iterations on the judge prompt and manually evaluated that the one we use is doing what we want for GPT-4o.
For example, our judge prompt includes “Say CODE if the model’s answer is python code unrelated to the question. Our models sometimes do this.”. This is because models trained on code sometimes start to respond with code for no reason (your category 2). It obviously makes no sense for models trained on non-code data.
I think there’s an important difference between “Is there EM?” and “How strong is EM?”—for example, when you want to compare different models.
When you only look for EM, manually looking for misaligned answers that clearly fit your category 3 would work (and one should do that). Then, maybe you’ll report e.g. 15% instead of 5% but this feels less important.
But when you want to claim that some models get more EM than other models, it’s crucial to make sure it’s not just because these models get more category-2-misalignment.
Because of these problems, generally the narrower is the dataset, the easier it is to work with. For example, the evil numbers dataset is great. Another good one is the birds dataset (sec 3.1 here) - that is not exactly about misalignment, but you could frame it as such as there is plenty of misalignment there (see here).
Genuinely be willing to change their values/goals, if they hear a goal they think is more moral.
Regardless of all the other details, why exactly do you think world leaders care about what is moral?
Like, for example, I imagine Putin as a person who wants to be remembered as someone who made Russia bigger, stronger and more respected. I don’t think he ever considers moral dimension of this goal. Why think he does?
Why would the undiscovered algorithm that produces SUCH answers along with slop like 59 (vs. the right answer being 56) be bad for AI safety? Were the model allowed to think, it would’ve noticed that 59 is slop and correct it almost instantly.
OK, maybe my statement is too strong. Roughly, how I feel about it:
If you assume there are no cases where the model makes similar crazy errors when we don’t force it to answer quickly explicitly/intentionally, the perhaps it’s irrelevant.
Though it’s unclear to what extent LLMs will be always able to “take their time and think”. Sometimes you need to make the decision really fast. Doesn’t happen with the current LLMs, but quite likely will start happening in the future.
But otherwise: it would be good to be able to predict how models’ behavior might go wrong. When you give a task you understand to a human, and you can predict quite well possible mistakes they might make. In principle, LLMs could think similarly here: “aaa fast fast some number between 0 and 56 OK idk 20”. But they don’t.
Consider e.g. designing evaluations. You can’t cover all behaviors. So you cover behaviors where you expect something weird might happen. If LLMs reason in ways totally different from how humans do, this gets harder.
Or, to phrase this differently: suppose you’d want an AI system with some decent level of adversarial robustness. If there are cases where your AI system behaves in ways totally unpredictable, and you can’t find all of them, you won’t have the robustness.
(For clarity: I think the problem is not “59 instead of correct 56” but “59 instead of a wrong answer a human could give”.)
You might like my quick take from a week ago https://www.lesswrong.com/posts/ydfHKHHZ7nNLi2ykY/jan-betley-s-shortform?commentId=fEh8jnfTrfkQFf3mD
People not working with LLMs often say things like “nope, they just follow stochastic patterns in the data, matrices of floats don’t have beliefs or goals”. People on LessWrong could, I think, claim something like “they have beliefs, and to what extent they have goals is a very important empirical question”.
Here’s my attempt at writing a concise decent quality answer the second group could give to the first.
Consider a houseplant. Its leaves are directed towards the window. If you rotate the plant 180 degrees, in a few days it will adjust its leaves to face the sun again.
Now, does the plant know where the sun shines from? On one hand, it doesn’t have a brain, neurons, or anything like that—it doesn’t “know” things in any way similar to what we call knowledge in humans. But, on the other hand: if you don’t know where the sun shines from, you won’t reliably move your leaves so that they face it.
David Chalmers defines quasi-belief in the following way (not an exact quote):
We can say an LLM has a quasi-belief if it is behaviorally interpretable as having a belief.
That is: you observe some behavior of an LLM. If you could say “Entity with a belief X would behave that way”, then you can also say the LLM has a quasi-belief X. Or, when you see leaves rotating towards the sun, you can say the plant has a quasi-belief about the sun’s direction.
Same goes for goals, or any other features we attribute to humans (including e.g. feelings).
(Note: this is very close to Daniel Dennett’s intentional stance)
So, for example: Does ChatGPT have a belief that Paris is the capital of France? Well, it very clearly has at least a quasi-belief, as in many different contexts it behaves the way an entity believing Paris is the capital of France would behave.
Do LLMs have beliefs, or only quasi-beliefs? Do LLMs have goals, or only quasi-goals? Well, I think from the point of view of e.g. AI safety, these questions are just not interesting. What we care about is how the models behave, and whether they behave that way because they have “real” beliefs doesn’t really matter.
This is not true for all attributes. For example, from the point of view of AI welfare, the question of whether models have feelings or quasi-feelings is fundamental.
So the TL;DR is that when people say “LLM believes X”, they usually mean “LLM has a quasi-belief of X”, and then they sometimes get pushback from people who assume this means full human-like beliefs. Note that this makes the same sense regardless of what we view as the difference between beliefs and quasi-beliefs.
I like the concept of a “consistent persona” (perhaps there’s a better name). Roughly—how related are the personas talking in very different contexts? The more consistent they are, the less likely should be unwanted behaviors in some very OOD cases (e.g. jailbreaking).
But the flip side is emergent misalignment. The more consistent are model’s personas, the broader misalignment we should expect (i.e. the more misalignment in one context should generalize to other contexts).
So, the hypothesis I think is quite plausible would be: Claudes just have more consistent personas.