I wrote this article for Communications of the ACM. It was published on 7 July 2026
Large language models can write essays, solve math problems, and generate computer code, but it’s not fully understood how they do it. Researchers can observe the billions of parameters inside these systems changing during training, yet the internal logic of the models remains largely hidden. In a sense, the engineering is ahead of the science. Can science catch up and make LLMs and other deep neural networks mechanistically interpretable?
Thomas Icard, a professor of philosophy and computer science at Stanford University, is contributing to this effort using tools from logic and cognitive science. He is a researcher in the growing field of mechanistic interpretability. “It’s striking how much progress there already has been in the last few years on basically every dimension,” Icard said, “from how much a model’s behaviors and representations reflect patterns in training data, and how post-training reshapes that behavior, to deep connections between internalized abstractions and generalization.”
Thomas Icard, a professor of philosophy and computer science at Stanford University, is contributing to this effort using tools from logic and cognitive science. He is a researcher in the growing field of mechanistic interpretability. “It’s striking how much progress there already has been in the last few years on basically every dimension,” Icard said, “from how much a model’s behaviors and representations reflect patterns in training data, and how post-training reshapes that behavior, to deep connections between internalized abstractions and generalization.”
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The full article can be read on the website of Communications of the ACM.
