Researchers have developed a new method called Mechanistic Tomography to improve the interpretability of AI models. This approach involves designing measurements to recover internal mechanisms and intervention effects within complex systems. The authors propose using a linear algebra framework to represent these measurements, which can be used to test and calibrate model predictions. They demonstrate the effectiveness of this method on various AI models, including GPT-2-small and Qwen-2.5-7B. --- Why it matters: This research matters because it provides a new tool for engineers and researchers to understand how complex AI systems work and make more accurate predictions. By improving interpretability, Mechanistic Tomography can help identify areas where AI models are flawed or biased, leading to better decision-making in applications such as control and intervention. Source: https://arxiv.org/abs/2608.19338