Determine generalizability of mechanistic interpretability findings across model families

Determine the extent to which mechanistic interpretability findings derived from CNN-based image models, BERT-based text models, and GPT-based language models generalize to other architectures and application contexts.

Background

Most mechanistic interpretability research has focused on CNNs and transformer families (BERT, GPT). Whether conclusions from these families transfer to alternative architectures and modalities is unclear.

As future frontier models diversify (e.g., multimodal or non-transformer architectures), understanding generalizability is important to ensure that methods and insights remain relevant and effective.