Very cool project directly addressing a problem area within mechanistic interpretability: visualizing information flow in transformers. By automating the process of identifying and color-coding attention head types, the tool lowers the barrier to entry for researchers and practitioners. It builds directly on established taxonomies from key papers in the field , demonstrating a strong understanding of the existing literature. The methodology is clearly explained and the application of the tool to known circuits provides strong validation. Well done! I would have liked to see a bit more discussion of safety implications, as it is currently somewhat high level - examples would be useful here, and perhaps some mention of computational cost. Expansions beyond the ones identified could also include incorporating methods for identifying polysemanticity in attention heads, which might help uncover more nuanced information flow..

This paper presents an interactive visualisation tool for attention patterns in transformer models. The authors demonstrate good understanding of the relevant literature and make circuit discovery more accessible and interactive. The tool has limitations in visualising MLP contributions (as highlighted by the authors) and may risk overinterpreting attention patterns, but it's still a really cool contribution!