Mechanistic Interpretability

Mechanistic interpretability is the attempt to understand a model's internals as mechanisms: features, circuits, attention heads, and activation pathways. It is different from asking a model for an explanation. The explanation might be plausible fiction. Mechanistic work inspects the tensors.

Features and superposition#

Neural networks appear to represent more features than they have obvious neurons. Superposition is the idea that many features are packed into overlapping directions in activation space. A single neuron is not necessarily "the sentiment neuron"; a feature may be distributed across many dimensions, and one dimension may participate in many features.

Sparse autoencoders#

Sparse autoencoders learn a dictionary of features that can reconstruct model activations using only a small number of active features at a time. Anthropic's "Golden Gate Claude" demonstration amplified a feature related to the Golden Gate Bridge and visibly changed model behavior. That was not a production control method; it was evidence that interpretable features can be found and manipulated.

Circuits#

Circuit work traces multi-step computation. Induction heads, for example, help models continue repeated patterns. Attribution graphs try to connect features across layers into causal pathways. The goal is to move from "this prompt failed" to "this internal feature path caused the failure."

Why it matters#

If interpretability matures, it could help with safety audits, debugging, deception detection, and model editing. Today it is still early. Results are real but partial, and methods that work on small circuits do not automatically explain a frontier model end to end.

Practical takeaways#

Treat mechanistic interpretability as a promising diagnostic science, not a turnkey monitoring product. It complements evals, red teaming, and behavioral tests. For production teams, the near-term use is learning failure modes and designing better tests, not claiming full transparency.