arXiv: cs.AI preprintsInternational6 November 2026
Understanding AI from the Inside: The State of Mechanistic Interpretability for AI Safety
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arXiv:2611.08438v1 Announce Type: cross Abstract: Neural networks are likely to be among the most consequential artefacts in human history. As these systems become more capable and autonomous, their potential to cause societal harm also grows, making AI safety one of the defining challenges of our time. Yet we continue to build increasingly powerful systems without fully understanding how they work, how they will behave in unfamiliar situations, or how to reliably control them. Mechanistic interpretability offers a path towards understanding these systems by studying their internal representat
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