Peering inside the black box: Mapping the internal neural circuits of large language models.

Why reverse-engineering neural networks down to individual circuits is the ultimate key to AI safety, alignment, and trust.

For years, the artificial intelligence community accepted a fundamental tradeoff: as neural networks became more powerful, they also became more opaque. Large Language Models (LLMs) with hundreds of billions of parameters perform stunning feats of reasoning, coding, and synthesis, yet even the engineers who built them cannot pinpoint the exact mathematical paths a model took to reach a specific answer.

We have built systems that work brilliantly, but operate as complete "black boxes."

In high-stakes domains—such as healthcare, autonomous defense, and corporate governance—relying on a system you cannot inspect is a massive liability.…