Interpretability and Mechanistic Analysis
This page tracks mechanistic interpretability, toy models, circuits, geometry, vector-symbolic views, and model introspection.
Sources in this batch
- Anthropic’s toy models of superposition and introspection work are direct mechanistic-interpretability sources.
- Transformer Circuits updates and linebreak/counting-task geometry sources examine internal representations.
- Spectral conditions for feature learning, GPT-2 through vector-symbolic architectures, Bayesian geometry of attention, and equivalent linear mappings broaden the theoretical toolkit.
- The seahorse emoji anomaly is an example of localized surprising behavior worth mechanistic explanation.
Research interest
The surprising trend is interpretability moving from toy superposition toward concrete cross-modal circuits, geometry of tasks, and introspective behavior. The open question is whether these analyses can predict or control failures before deployment, rather than explaining them after the fact.
Open questions:
- Which interpretability claims transfer across architectures and scales?
- Can introspection be mechanistically grounded rather than behaviorally prompted?
- Are anomalies like linebreaks/counting/seahorse failures windows into general representation pathologies?