Computation
The circuits and causal pathways that read internal representations, transform them, and write the results toward output, and the methods that establish those pathways causally.
02.1 What this programme studies
Knowing what a model represents is not knowing what it computes. This programme studies the mechanisms: attention heads that route information between positions, MLPs that transform it, and the compositions of both: circuits that implement identifiable algorithms across layers.
The methodological core is causal intervention. Activation patching, causal tracing, attribution graphs, and model editing all answer the same underlying question: if this component's contribution is changed, does the behaviour change as predicted? Correlation level evidence (attention weights, probe accuracy, plausible stories) does not settle mechanism, and this programme treats the gap between the two as its central discipline.
A specific concern of the institute sits here: the relationship between a model's verbalised reasoning and its actual internal computation. Chain of thought is an output, produced by the same machinery it purports to describe. Where the two diverge, only circuit level evidence can say so.
02.2 Topics
Attention & MLP circuits
Head composition, induction, name moving, and the algorithmic roles of individual components.
Attribution graphs
Tracing which upstream features cause which downstream ones through replacement models.
Activation patching
Swapping activations between runs to localise where behaviour relevant computation happens.
Causal tracing & circuit discovery
Automated and manual identification of the subgraph that suffices for a behaviour.
Information flow across layers
The residual stream as a communication bus: who reads, who writes, and when.
Hidden vs verbalised computation
Where chain of thought matches the mechanism, and where it demonstrably does not.
Model editing & causal intervention
Editing stored associations as an existence proof of localised computation.
02.3 Open questions questions, not findings
- How much of a given behaviour is explained by the circuits we can currently find, and what carries the remainder?
- Do discovered circuits generalise beyond the task distribution they were found on?
- Can attribution methods scale from single behaviours to the routine auditing of a frontier model?
- When chain of thought and internal computation diverge, is the divergence systematic and predictable?
Methods in use or proposed: Activation patching · path patching · attribution graphs · ablation studies · model editing · TransformerLens based replication.
02.4 Research objects in this programme
02.5 Key literature
A short orientation list of primary sources, not a survey. The Interpretability Map holds the full dependency graph.
- Elhage, N., et al. (2021). A Mathematical Framework for Transformer Circuits. Transformer Circuits Thread. transformer-circuits.pub/2021/framework/index.html
- Olsson, C., et al. (2022). In context Learning and Induction Heads. Transformer Circuits Thread. transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html
- Wang, K., et al. (2022). Interpretability in the Wild: A Circuit for Indirect Object Identification in GPT-2 Small. arxiv.org/abs/2211.00593
- Meng, K., Bau, D., Andonian, A., & Belinkov, Y. (2022). Locating and Editing Factual Associations in GPT. NeurIPS 2022. arxiv.org/abs/2202.05262
- Conmy, A., et al. (2023). Towards Automated Circuit Discovery for Mechanistic Interpretability. NeurIPS 2023. arxiv.org/abs/2304.14997
- Heimersheim, S., & Nanda, N. (2024). How to Use and Interpret Activation Patching. arxiv.org/abs/2404.15255
- Lindsey, J., et al. (2025). On the Biology of a Large Language Model. Transformer Circuits Thread. transformer-circuits.pub/2025/attribution-graphs/biology.html