vision

Catherine Yeh; Yida Chen; Aoyu Wu; Cynthia Chen; Fernanda Viégas; Martin Wattenberg · Harvard University

Visualises a joint embedding of the query and key vectors a transformer uses to compute attention, which makes it possible to see global patterns across many input sequences rather than one prompt at a time.

Shifted attention visualisation from single-example to distribution-level — the exact move Anthropic's HeadVis names as its closest prior work three years later.

High · Read the arXiv abstract page in full, 2026-08-06. The live demo at attentionviz.com was not inspected — client-rendered, fetch returned an empty page shell.

Shan Carter; Zan Armstrong; Ludwig Schubert; Ian Johnson; Chris Olah · Google Brain; OpenAI

Renders millions of activations from an image classifier as feature-inversion images laid out on a single navigable map, so you can pan across the concepts a network has learned the way you would read an atlas.

Made a model's whole learned concept space visible at once rather than one neuron at a time — the clearest ancestor of the feature-neighbourhood maps in Scaling Monosemanticity.

High · Read the Distill article and its citation metadata, 2026-08-06

Chris Olah; Arvind Satyanarayan; Ian Johnson; Shan Carter; Ludwig Schubert; Katherine Ye; Alexander Mordvintsev · Google Brain

The founding text for interactive interpretability. Argues that interpretability techniques studied in isolation are far weaker than the interfaces you get by composing them, and demonstrates this with a set of live, hoverable interfaces over an image classifier.

Established that the interface IS the contribution — the template every entry in this ledger inherits from.

High · Read in full (article + Distill metadata), 2026-08-06