Researchers develop Maximally-Activating Example Metamodels (MAEMs), language models that map a residual-stream direction from a subject model to text exemplars strongly activating it. A label-free reinforcement-learning objective maximises cosine similarity between the supplied direction and activations produced by MAEM-generated text, avoiding corpus limits.
Why it matters
Corpus scanning cannot reveal activations for rare directions; generating examples could make interpretability cover the full residual stream.
Analysis
Interpretability may move from searching existing text to generating targeted probes for model internals.
Source: arXiv
AI disclosure: this article was produced with AI assistance and reviewed for accuracy.
