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transformer-circuits

topic2 events
papersSEP 12 13:56 UTC

Anthropic Paper Proposes Mathematical Framework for Analyzing Transformer Circuits

Anthropic researchers published a paper outlining a mathematical approach to reverse-engineering how transformer models compute internally, treating attention heads and MLP layers as composable circuits. The framework aims to make the internal mechanisms of these models more tractable to study and explain. It is intended as a foundation for interpretability work rather than a description of any specific deployed system.

interpretability/transformers/research/anthropic
papersSEP 10 04:00 UTC

Training trajectories determine circuit removability in annealable soft-prior Transformers

Researchers asked whether retrieval circuits that small Transformers learn with the help of soft positional priors keep functioning once that prior is taken away. They tested this using a model whose prior-based attention biases can be gradually annealed out during training. The results indicate that the specific training trajectory, not just the architecture, decides whether a learned circuit can stand on its own after the prior is removed.

interpretability/transformers/training-dynamics/retrieval