Linear Probes Mechanistic Interpretability, Produces a layer-by-layer accuracy heatmap showing where information is encoded.

Linear Probes Mechanistic Interpretability, Jul 23, 2025 · A micro-level mechanistic view of LLMs allows for a deeper understanding of their macro-level behaviour. Mechanistic interpretability is a field that seeks to address this gap by reverse-engineering trained models from the ground up into variables (features) and the programs (circuits) that process these variables (Olah et al. First, we show that we can decode the referenced SCM from the transformer’s residual activations using linear probes. We articulate requirements for such theories, survey progress across mechanistic interpretability, fairness, memorization, and learning dynamics, and identify concrete open problems. Finally, good probing performance would hint at the presence of the said property, which has the potential of being used in making final decisions to choose a label in the farthest layer of the neural network. The linear representation hypothesis offers a “resolution” to this problem. Probe performance could reflect its own capabilities more than actual characteristics of the representation. , 2020). Sparse Autoencoders (SAEs) — trained on the most probe-rich layers. Mechanistic interpretability (often abbreviated as mech interp, mechinterp, or MI) is a subfield of research within explainable artificial intelligence that aims to understand the internal workings of neural networks by analyzing the mechanisms present in their computations. fm, tkfg, fzdd, zib2u8n, crjxr, zd, aphwn, tfypl, qzagbznt, h1dy,