ImageNet Classification with Deep Convolutional Neural Networks
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
THE RESEARCH ATLAS
A “diff” tool for AI: Finding behavioral differences in new models
An interpretability research overview explores ways to identify behavior differences between AI models beyond predefined tests.
Institution and location sources
Location refers to credited institutions or the listed organization, not author citizenship or an exhaustive collaboration list.
Why read this
This entry links to the official research article and its underlying paper; it is labeled as an article.
Citation
@misc{team2026diff,
title = {A “diff” tool for AI: Finding behavioral differences in new models},
author = {Anthropic Interpretability Team},
year = {2026},
url = {https://www.anthropic.com/research/diff-tool}
}Rights remain with the original authors. Refer to the original source for citations and licensing.
Related research
Attention Is All You Need
The Transformer replaces recurrent sequence processing with attention, creating a foundation for many later language and multimodal models.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Masked-language pretraining learns bidirectional representations that can be adapted to language-understanding tasks.