Interpretable Circuits Explained: How OpenAI’s Sparse Transformers Demystify Neural Networks

Understanding Neural Networks Through Sparse Circuits: A Deep Dive into OpenAI’s 2025 Breakthrough Neural networks power some of the most advanced AI systems today, but their inner workings remain largely mysterious. We train these models by adjusting billions of connections, or weights, until they excel at tasks, but the resulting behaviors emerge in ways that are hard to decipher. In late 2025, OpenAI released groundbreaking research titled “Weight-sparse transformers have interpretable circuits” (Gao et al., 2025), introducing a novel approach to make models more transparent. By training weight-sparse Transformers—models where most weights are forced to zero—they created networks with clearer, …