This paper by OpenAI discusses a new approach to **neural network interpretability** through the use of **sparse circuits**. The authors explain that understanding the behavior of complex, hard-to-decipher neural networks is critical for safety and oversight as AI systems become more capable. They distinguish their work on **mechanistic interpretability**, which seeks to fully reverse-engineer computations, from other methods like chain-of-thought interpretability. The core of their research involves training **sparse models**—models with far fewer internal connections—to create simpler, **disentangled circuits** that are easier to analyze and understand, offering a promising path toward making even larger AI systems transparent.

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