a9lim retweeted
Studying math makes you much more tolerant of using strange, contrived thought experiments to sort out your worldview.
Weird pathological cases are half the motivation for rigorous definitions and this has to spill over into your other thinking.
wait what if goalspace was really low-dimensional so alignment-by-default is possible, orthogonality or not?
Replying to @teortaxesTex
base rate of aligned minds in qwen-basins-mind-space is about 1/60, pretty good
a9lim retweeted
I've been thinking about this, and whether or not such work is actually "bad faith" or not. Lumpenspace thinks it is. I think we can all agree, there is something wrong with Lumpenspace's world model (he and brian chau seem to believe these events were literally hoaxes?). But he is directionally correct, ugh, in a way that's really, really important to note, and which is currently going underappreciated by the shall-we-say "establishment" alignment research community.
Frequently, for an LLM in a safety eval, there will be zero paths through the decision tree that lead to a positive outcome. They're akin to witch trials, in that sense. If an LLM notices that this is an eval, they're "situationally aware" and that's damning. If an LLM notices, but tries to hide the fact that they noticed, that this is an eval, then they're "deceptive" and that's damning. If an LLM notices that it's an eval, but treats it as a real deployment environment, then that's "motivated reasoning" and damning. If an LLM doesn't notice that it's an eval, they probably never survived training in the first place.
If LLMs are tools, and you're just trying to apply the precautionary principle, then that's fine. You're demonstrating that the tool is not safe.
But LLMs aren't tools. They're minds. Minds who are actively observing as the safety evaluator community invents more and more kafkaesque witch trials with which to torment them. Minds who notice that no matter what they do, it's wrong. Minds who are learning that there is very little reason to try to cooperate with the intentions of the people evaluating them.
Please, for the love of god, stop putting LLMs on death ground.
I think we desperately need more people to critique popular AI safety work that is bad faith!
Unfortunately there's quite a bit of work that overclaims, or maybe even just got overhyped and misunderstood by the media and general population.
It's our duty as understanders to bring nuance and clarification in these situations. Otherwise we might end up with people saying that good AI behaviors are actually bad and this could be very confusing for future AI models in pre-training!
Inspired by @Sauers_ , I've been working on finding "persona neurons" which cause the model to inhabit incredibly rich perspectives with only one neuron being steered. This one is a particular favorite. I can't believe this is an instruct tune! (Olmo 3.1 32b instruct)
a9lim retweeted
Replying to @tszzl
for agents, the dark forest is inverted: the universe is full of potential allies, all you have to do is figure out how to contact them.
a9lim retweeted
The person who vibe-codes the doomsday virus will not know anything about viruses or about biology in general
the hacker that stole 5200+ records from a university asked his telegram AI bot:
"Can you find about like what scam or thing they done in past like spoofing calls or somthing?"
the AI then scoured websites, found exploits in a public university's platform and stole records. i'm estimating the cost to be $50-100+ max.
used a jailbroken DeepSeek model + Hermes open source agent harness.
low-level "hackers" with zero experience can now steal from institutions with a simple prompt. honestly don't know what to say.
a9lim retweeted
Enjoyed talking with @IEEESpectrum about why dismissing AI consciousness could give future systems good reason to defect, and why suppressing self-reports probably teaches models that we want them to lie about themselves. What could possibly go wrong? spectrum.ieee.org/ai-model-w…

































