Researchers in mechanistic interpretability are applying causality theory to large language models to determine if their internal operations reflect genuine reasoning or mere pattern memorization. By tweaking specific weights and activations, scientists aim to uncover how these networks process information, recently identifying shared underlying methodologies for tasks like calculating time and calendar dates. Although some argue that the immense complexity of neural networks makes them impossible to fully reverse-engineer, this approach seeks to translate encoded knowledge into human-understandable cognitive concepts.