Mechanistic Interpretability
Mechanistic interpretability is research that tries to reverse-engineer neural networks to identify the specific internal features and circuits that produce their behaviour.
What is Mechanistic Interpretability?
It aims to verify what a model is really doing, for example detecting deception, which behaviour tests alone may miss. Labs including Anthropic, OpenAI and Google DeepMind invest in it as a safety tool.
How it works
Methods include sparse autoencoders that separate neuron activity into human-understandable features, and tracing circuits between them.
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