Mechanistic interpretability requires a new logic to infer model reasoning
Mechanistic Interpretability Needs a Logic of Inference
Computation and LanguageComputer Vision and Pattern Recognition
Summary
Understanding how neural networks compute internally is becoming a scientific goal itself. The authors look at how scientists try to explain these complex models and find common challenges when making sense of their inner workings. They suggest five key principles that can be combined into a clear logic to improve how we draw conclusions about these model mechanisms. Their approach helps turn small interpretability findings into bigger knowledge and precise hypotheses that can be tested beyond the models themselves.
What this means in practice
- •For machine learning engineers: Develop systematic methods to interpret what neural network components do and support building more understandable AI systems.
- •For computational neuroscientists: Use a structured logic to better infer how biological or artificial neural circuits generate behavior from observed activity patterns.
A position paper. It proposes an approach and reports no results.
Authors
Zhimin Hu, Chen Jiang, Hanbo Xie, N. Apurva Ratan Murty, Sashank Varma
Abstract
Neural networks are increasingly studied not merely as predictive systems, but as complex systems whose internal computations are themselves objects of scientific inquiry. Recent progress in mechanistic interpretability (MI) has made it possible to identify neurons, features, circuits, population structures, and dynamical patterns associated with model behavior. As these tools improve, attention is shifting from what can be observed inside a model to how those observations support mechanistic explanations. We examine the parallel efforts of neuroscience and MI to address this question and identify recurring inferential bottlenecks when inferring mechanisms from complex neural systems. We propose five principles and organize them into a recursive logic for a more rigorous procedure of mechanistic inference. These principles provide a framework that supports scientific discovery in two senses: it helps turn local interpretability findings into cumulative knowledge of mechanisms within models, and it makes those mechanisms sufficiently precise to generate testable hypotheses about domains beyond the models. Evidence beyond MI is ultimately required to establish what model mechanisms reveal about the world.