A field trip through the inner world of an LLM we don’t fully understand
This title could be clearer and more informative.Try out Clickbait Shieldfor free (5 uses left this month).
A deep-dive into the internal mechanics of large language models, exploring how they 'think' through the lens of mechanistic interpretability research. Covers key concepts including Riemannian manifolds as state spaces, polysemanticity and superposition in neurons, hallucination basins as geometric phenomena in latent space, world models emerging from training, and the lack of true working memory. The author connects these internals to practical enterprise AI governance, arguing that hallucinations are not random bugs but structured geometric failures that can be monitored and intercepted pre-inference using trajectory-based approaches like their Ontological Compliance Gateway.
