That paper is fucking hilarious, but the tl;dr is that when asked to manage a vending machine business for an extended period of time, they eventually go completely insane. Some have an existential crisis, some call the whole thing a conspiracy and call the FBI, etc. it’s amazing how trash they are.
Initial thought: Well… but this is a transparently absurd way to set up an ML system to manage a vending machine. I mean it is a useful data point I guess, but to me it leads to the conclusion “Even though LLMs sound to humans like they know what they’re doing, they does not, don’t just stick the whole situation into the LLM input and expect good decisions and strategies to come out of the output, you have to embed it into a more capable and structured system for any good to come of it.”
Updated thought, after reading a little bit of the paper: Holy Christ on a pancake. Is this architecture what people have been meaning by “AI agents” this whole time I’ve been hearing about them? Yeah this isn’t going to work. What the fuck, of course it goes insane over time. I stand corrected, I guess, this is valid research pointing out the stupidity of basically putting the LLM in the driver’s seat of something even more complicated than the stuff it’s already been shown to fuck up, and hoping that goes okay.
Edit: Final thought, after reading more of the paper: Okay, now I’m back closer to the original reaction. I’ve done stuff like this before, this is not how you do it. Have it output JSON, have some tolerance and retries in the framework code for parsing the JSON, be more careful with the prompts to make sure that it’s set up for success, definitely don’t include all the damn history in the context up to the full wildly-inflated context window to send it off the rails, basically, be a lot more careful with how to set it up than this, and put a lot more limits on how much you are asking of the LLM so that it can actually succeed within the little box you’ve put it in. I am not at all surprised that this setup went off the rails in hilarious fashion (and it really is hilarious, you should read). Anyway that’s what LLMs do. I don’t know if this is because the researchers didn’t know any better, or because they were deliberately setting up the framework around the LLM to produce bad results, or because this stupid approach really is the state of the art right now, but this is not how you do it. I actually am a little bit skeptical about whether you even could set up a framework for a current-generation LLM that would enable to succeed at an objective and pretty frickin’ complicated task like they set it up for here, but regardless, this wasn’t a fair test. If it was meant as a test of “are LLMs capable of AGI all on their own regardless of the setup like humans generally are,” then congratulations, you learned the answer is no. But you could have framed it a little more directly to talk about that being the answer instead of setting up a poorly-designed agent framework to be involved in it.
LLMs can degrade by giving “wrong” answers, but not because of network congestion ofc.
That paper is fucking hilarious, but the tl;dr is that when asked to manage a vending machine business for an extended period of time, they eventually go completely insane. Some have an existential crisis, some call the whole thing a conspiracy and call the FBI, etc. it’s amazing how trash they are.
Initial thought: Well… but this is a transparently absurd way to set up an ML system to manage a vending machine. I mean it is a useful data point I guess, but to me it leads to the conclusion “Even though LLMs sound to humans like they know what they’re doing, they does not, don’t just stick the whole situation into the LLM input and expect good decisions and strategies to come out of the output, you have to embed it into a more capable and structured system for any good to come of it.”
Updated thought, after reading a little bit of the paper: Holy Christ on a pancake. Is this architecture what people have been meaning by “AI agents” this whole time I’ve been hearing about them? Yeah this isn’t going to work. What the fuck, of course it goes insane over time. I stand corrected, I guess, this is valid research pointing out the stupidity of basically putting the LLM in the driver’s seat of something even more complicated than the stuff it’s already been shown to fuck up, and hoping that goes okay.
Edit: Final thought, after reading more of the paper: Okay, now I’m back closer to the original reaction. I’ve done stuff like this before, this is not how you do it. Have it output JSON, have some tolerance and retries in the framework code for parsing the JSON, be more careful with the prompts to make sure that it’s set up for success, definitely don’t include all the damn history in the context up to the full wildly-inflated context window to send it off the rails, basically, be a lot more careful with how to set it up than this, and put a lot more limits on how much you are asking of the LLM so that it can actually succeed within the little box you’ve put it in. I am not at all surprised that this setup went off the rails in hilarious fashion (and it really is hilarious, you should read). Anyway that’s what LLMs do. I don’t know if this is because the researchers didn’t know any better, or because they were deliberately setting up the framework around the LLM to produce bad results, or because this stupid approach really is the state of the art right now, but this is not how you do it. I actually am a little bit skeptical about whether you even could set up a framework for a current-generation LLM that would enable to succeed at an objective and pretty frickin’ complicated task like they set it up for here, but regardless, this wasn’t a fair test. If it was meant as a test of “are LLMs capable of AGI all on their own regardless of the setup like humans generally are,” then congratulations, you learned the answer is no. But you could have framed it a little more directly to talk about that being the answer instead of setting up a poorly-designed agent framework to be involved in it.