Transformer Circuits Thread

Can we reverse engineer transformer language models into human-understandable computer programs?

Transformer Circuits Thread --> Transformer Circuits Thread Anthropic’s Interpretability Research A surprising fact about modern large language models is that nobody really knows how they work internally. The Interpretability team strives to change that — to understand these models to better plan for a future of safe AI. May 2026 Circuits Updates — May 2026 A short update on understanding features through downstream connections. Natural Language Autoencoders Produce Unsupervised Explanations of LLM Activations Fraser-Taliente, Kantamneni, Ong, et al., 2026 We train Claude to translate its inte

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• Bryan Chiang
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related reading

• A Mathematical Framework for Transformer Circuitstransformer-circuits.pub
• On the Biology of a Large Language Modeltransformer-circuits.pub
• Towards Monosemanticity: Decomposing Language Models With Dictionary Learningtransformer-circuits.pub
• An Extremely Opinionated Annotated List of My Favourite Mechanistic Interpretability Papers v2 — AI Alignment Forumalignmentforum.org
• Circuit Tracing: Revealing Computational Graphs in Language Modelstransformer-circuits.pub
• Tracing the thoughts of a large language model \ Anthropicanthropic.com
• In-context Learning and Induction Headstransformer-circuits.pub
• Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnettransformer-circuits.pub
• An Extremely Opinionated Annotated List of My Favourite Mechanistic Interpretability Papers v2 — AI Alignment Forumalignmentforum.org
• Mapping the Mind of a Large Language Model \ Anthropicanthropic.com
• Circuits Updates - January 2024transformer-circuits.pub
• Transformer Explainer: LLM Transformer Model Visually Explainedpoloclub.github.io