# Interpretability Gets Automated

Interpretability progress increasingly comes from using language models to automate the labor-intensive human steps in existing analysis methods rather than from new mathematics, turning circuit-level analysis from a per-behavior study into something that can run at production scale.

- Conviction: 40 / 100 (forming)
- Horizon: Emerging (watchlist)
- Tracking since: 2026-08-05T00:00:00.000Z
- Last updated: 2026-08-05T05:44:40.671Z
- Canonical: https://polylog.news/ai/trends/automating-interpretability
- Publisher: Polylog
- Affected regions: United States

## Recent evidence

- [confirms] A New Pipeline Uses Language Models to Do the Manual Work in Circuit Tracing (2026-08-05): Researchers report a pipeline in which language models group features and neurons into supernodes for attribution-graph analysis, the step that has been the human bottleneck in circuit tracing. Removing the manual annotation stage is precisely the labor substitution the thesis names, and it is what would let circuit-level analysis run per-deployment rather than per-paper.