“The only reason I believe other humans feel pain is that they start to cry or they shudder or whatever the thing is. That's the only reason anyone believes that anybody else feels pain.”

Yeah, it's another one in a long line of papers and results where, look, we'll never get dispositive evidence that LLMs can actually feel pain or any emotion. We're never going to get that. And just like.

I think even like feeling pain is underdefined in a sense. We don't know what consciousness is. Nobody knows. Nobody defines it. So when we say pain here or feeling pain, we mean sort of like roughly something akin to what we would say feeling. It's a correlate, right? Yeah.

be fair, so are we when you go to the office and told to do boring work? I don't know if it's not comparable, but the paper itself is quite good, I think. As a read of this kind, it's not like a philosophical paper. It is technical and it doesn't like it positions as pain, but not in a sort of very theoretical sense. It's actually doing quite good work, I would say, on putting forth the case that this is a valid example of a pain representation within models. And moving on to research and advancements that doesn't have to do with safety. First, we are going to catch up on something we haven't covered previously, which is OpenAI cracking the millennium problem. I don't think we'll go super deep here because it'll take a while to explain. It is a millennium math problem. The very, very short version is there is a pretty important set of equations, the Navier-Stokes equations having to do with fluid dynamics, I think. Actually, I don't know too much about this stuff, but there's been a question of whether they kind of break in some conditions, right? And what OpenAI demonstrated is you can find ways in which they kind of blow up, break, don't make sense, which, you know, in some kind of conceptual sense might tell you that the equations don't model reality, which we already know, but it's still useful insofar as increasing our understanding. And a millennium problem is one of these problems that you can get $1 million for solving. So it's a big deal. It's not like they solved some famous math problem. They solved one of the math problems that it didn't put forth, at least one aspect of it or one kind of case of it that argument is a bit simpler. There's a lot to discuss about how this happened and it was a mess. So we indicated previously there was a bit of contention about whether OpenAI had indirectly made use of a mathematician's work. There was kind of some discussion with codecs that may or may not have gone into training data, a bit ambiguous. The short version on that is, from what I could tell, there's not much evidence that OpenAI, like this seems to be just a million agents getting a good math result. There's no sort of nefarious stuff here. And the rollout of announcement was also a bit not perfect. It was kind of like a math announcement as PR. We didn't engage mathematicians very much ahead of time. They made it a big loud splash because they thought that Anthropic may be heading in this direction. So they spun up 10,000 agents, got result, put it online, and that has had kind of rippling effects in the market. Mathematics community ever since. Some calls for slowdown from figures such as such as standard style, OpenAI now getting an advisory committee or something of mathematicians. So, this really, I think, is notable not just as a major breakthrough, but also as this, I don't know, transition point where mathematicians and frontier companies similar to cybersecurity need to figure out what to do now.

Yeah. Yeah. And I think a warning shot of a pattern of escalatory dynamics that I don't think people are pricing in right now is including between US and China. Imagine a world where everyone agrees not to do a certain kind of thing, but then a rumor just spreads that overnight the, you know, America's leading frontier model just did the thing and broke ranks. And now they have this crazy, you know, deathball technology. And it turned out not to be the case. It turned out not to be the case that Anthropic had actually done what OpenAI thought they did. And what prompted OpenAI to dump presumably tens of millions of dollars into solving Navier-Stokes and a whole bunch of other things that it seems they're sitting on, maybe as much as 100 major math results they claim of Millennium Prize level or sort of similar. This is a really dicey area, right? Like when you're talking about math, everything sounds fine because it sounds like a just positive something. But imagine the same thing happening with destabilizing weapons grade technology, which we are absolutely capable of doing at this point. You know, rumors can really fly. And so, yeah, interesting that this was actually based on a rumor. It also tells us like how many other problems like this could we solve with $50 million today? Like it's not at all obvious that there aren't many, many, many more, including things in domains like material science, additive manufacturing, again, profoundly destabilizing if they happen. And so anyway, you know, not to be all doom and gloom, it just, you know, the world has adapted to a certain set of assumptions about what is doable and in how long using how much money and how much talent. Those assumptions are being shattered in real time. And that means the careful, precarious equilibrium that we are in. I don't mean to sound like a Berkeyan. I'm like, I kind of am, but like the delicate equilibrium that we are in is a great risk. Like you can't just keep inventing crazy new technologies every Tuesday and think that stability will win through. So anyway, I thought it was interesting, by the way, seeing that short clip of Terence Tao reacting to this almost viscerally in the moment. He said, we probably need to slow this down. Like there's no reason for things to be going this fast. It was something like that that he said after having been really bullish on AI, like literally, as far as I know, a couple months ago, publicly, it kind of makes you think, you know, like everybody almost needs to have a kind of Pearl Harbor moment that is so incredibly specific to their domain before they will sort of recognize what everybody else who's been hit by it sees. It seems like just a positive until suddenly it directly threatens your line of work. Not that it is negative in this case, it's just complicated.

And next paper, scaling discovery through test time communication. So this is about multi-agent systems and how you can get better results by having multiple agents interacting and collaborating. In this case, we have a simple paradigm of basically like a chat room. So we have an append-only communication log. You launch a set of agents and they can all work together to solve things like Arc AGI free. So basically like solving actual problems. Also, we have examples like polio mino packing, amnest compression, and they demonstrate that having multiple agents that can communicate with each other via a sample like chat room winds up working much better than having multiple agents working in parallel. And then you can just take the best result. So they have this comparison of team at K versus best at K and demonstrate quite impressive improvements in some of these tasks like Arc AGI3 from having multiple agents versus not having agents. This one made a bit of waves on Twitter, I know, and it's easy to sort of see this as, oh, now we have scaling via agents. We already had scaling via agents. We actually covered this paper towards the science of scaling agent systems over a year ago, a year and a half ago almost from DeepMind. So the idea of scaling systems and collaborating agents, just to be clear, isn't new here. But kind of the specific settings they tried it on, such as Arc AGI3 and the way they did it and the model kind of newness they did it with are all pretty new. And there's some good empirical results here.