Reproducible tools and evidence for sparse autoencoder research
AI & ML interests
Building open foundations for understanding, evaluating, and intervening in neural networks.
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Reproducible tools and evidence for sparse autoencoder research
Benchmarks, datasets, and pretrained models for reproducible sparse autoencoder research, feature interventions, and mechanistic interpretability.
Research, benchmarks, and tools for measuring how sparse autoencoder features interact to shape model behavior.