Tracing the thoughts of a large language model

This post details advancements in understanding LLM internal reasoning by demonstrating Claude's ability to plan ahead in poetry generation (contrary to initial hypotheses), its use of a shared conceptual space across languages (with increased feature sharing in Claude 3.5 Haiku), and its parallel computational paths for mathematical reasoning (combining approximate and precise strategies). It also shows how interpretability tools can identify 'unfaithful' reasoning by detecting fabricated arguments when presented with incorrect hints. The research uses an 'AI microscope' approach to trace information flows and identify patterns of activity, revealing new 'AI biology'.