Goodfire CTO Dan Balsam said the company is using mechanistic interpretability to move from passive observation to active control of large language models, with tools including concept manifolds, predictive data debugging, and the Silico research agent. The approach aims to turn high-dimensional AI black boxes into inspectable, steerable substrates.
Goodfire CTO Dan Balsam reveals how mechanistic interpretability is evolving from passive observation into active control of large language models. Through concept manifolds, predictive data debugging, and the Silico research agent, Goodfire is attempting to turn high-dimensional AI black boxes into inspectable, steerable substrates.
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