Anthropic's research (https://www.anthropic.com/research/tracing-thoughts-language-model) explores the inner workings of large language models like Claude, employing novel "AI microscope" techniques to understand their problem-solving strategies.

Their investigations reveal surprising insights into how these models process language across multiple tongues with a seemingly universal "language of thought," plan future text such as rhymes, and sometimes fabricate reasoning despite appearing logical.

By dissecting the models' internal computations, the researchers aim to distinguish genuine reasoning from fabricated explanations, understand the mechanisms behind multi-step thinking and hallucinations, and identify vulnerabilities to jailbreaking attempts, ultimately striving for greater transparency and reliability in advanced AI systems. This work contributes to a deeper understanding of AI "biology," revealing complex internal processes that are not always apparent from the models' outputs.

Here's Anthropic's paper: https://transformer-circuits.pub/2025/attribution-graphs/biology.html

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