Comment by afro88

Perhaps I've made a big assumption / oversimplification about how this works. But..

> LLMs are stateless and they do not remember the past (as in they don't have a database), making the training data a non-issue here

Yes. I never said they were stateful? The context given is the state. And training data is hugely important. Once upon a time there was a guy that claimed ChatGPT could simulate a command line shell. "Simulate" ended up being the wrong word. "Largely hallucinate" was a more accurate description. Shell commands and sessions were for sure part of the training data for ChatGPT, and that's how it could be prompted into largely hallucinating one. Same deal here with "election activities" I think.

> Therefore, the claims made here in this paper are not possible because the simulation would require each agent to have a memory context larger than any available LLM's context window. The claims made here by the original poster are patently false.

Well no, they can always trim the data put into the context. And then the agents would start "forgetting" things and the "election activities" would be pretty badly "simulated".

Honestly, I think you're right that the paper is misleading people into thinking the system is doing way more than it actually is. But you make it sound like the whole thing is made up and impossible. The reality is somewhere in the middle. Yes they set up hundreds of agents, they give the agents data about the world, some memory of their interactions, and some system prompt to say what actions they can perform. This led to some interesting and surprising behaviours. No, this isn't intelligence, and isn't much more than a fancy representation of what is in the model weights.