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Nice writeup. I wasn't even aware k-means clustering can be viewed from the Variational Bayes framework. In case more perspectives are useful to any readers: When I first tried to learn about this, I found the Pyro Introduction very helpful; because it is split up over a lot of files, I put together these slides for Bayesian Neural Networks, which also start out with a motivation for Variational Bayes.

I've been thinking about alignment of subsystems in a very similar style and am really excited to see someone  else thinking along this way. I started a comment with my own thoughts on this approach; but it got out of hand quickly; so I made a separate post: https://www.lesswrong.com/posts/AZfq4jLjqsrt5fjGz/formalizing-alignment 

Would be keen on having any sort of feedback.

Thanks for the pointers! The overviews in both sources are great. I especially like Rumelhart's Story Grammar. Though from what I gather from Mark Riedl's post is that the field is mostly about structure/grammar inherent to stories as objects that exist pretty much in a vacuum, and does not explicitly focus on making connections to some sort of models of agents that communicate using these stories.