Tracing the Thoughts of a Large Language Model
Introduction
Full credit goes to the original author, linked below. All blog posts were reposted either with permission of the author, or by anonymous submission by SAIRC members like yourself.
Language models learn their own problem-solving strategies during training, encoded in billions of inscrutable computations. This interpretability work builds an "AI microscope" to trace those internal steps in Claude — probing questions like how it works across dozens of languages and whether it plans ahead when writing — so researchers can better understand what models are actually doing and verify they are doing what we intend.