The Next Step Beyond LLMs: Foundation Models for Inference

Today's clip is from episode 161 , featuring Luigi Acerbi. In this conversation, Luigi explains one of the biggest engineering bottlenecks facing transformer-based probabilistic models—and how his group found a way around it. The core challenge is that many inference models treat data as an unordered set, making them naturally permutation invariant. That's statistically elegant, but…