How Can We Change Agent Behavior? (Revisited) Ask 25 language models to write a metaphor involving time, and most of them will give you variations of “Time is a River,” and occasionally “Time is a weaver.” The models seem to be drawn into these attractors that make their responses predictable. Can architecture overcome this pull? In my first blog post “Are All AI Models Secretly Speaking the Same Language?”, we encountered the idea that embeddings created a multi dimension vector space that captured the relationship between words and ideas. It seemed that the geometry of the vector spaces of different AI models (and even the brain) all share a certain symmetry. I attributed this to the fact that they reflect the same underlying reality. I recently came across an article in MIT Technology Review, “ LLMs are stuck in a groupthink groove. This startup is trying to get them out. ”. It talks about how the current LLMs are prone to a kind of Hivemind, where different models all come up ...