brightlabYear 6 · Explicit teaching model · 35 minutes
Pip’s picnic: follow the clues
When should a new clue change a guess?
The first load downloads Python and may take a moment. Write a prediction, then open the lab. Controls, help, teacher notes and your evidence download are inside. If the school network blocks the runtime, download the Python notebook and run it with your teacher.
Investigate one research connection
Stanford Guest Lectures: AP293 (Fall 2025)
Describe a clue, a guess and a test in different sentences. Use the linked notebook to make two observations. Draw or describe one result and one thing this activity cannot tell us about the source system.
This entry reviews the article and lecture outlines, not a full transcription of the videos.
Use the notebook below to collect the measurements. This writing remains in this page session until downloaded.
Teacher background and source method
Three interpretability lecture outlines
Introduces causal abstraction, circuits and learning in context; a teaching guide rather than one dataset.
Independent classroom adaptation; not a reproduction of the source model or complete method.
Read the source with a teacher ↗Opening the notebook page…
First use downloads Python and its libraries. A fresh session measured about 20–22 seconds and 14–17 MB during the initial audit; school networks vary. This hosted notebook computes on the browser CPU.
Your prediction and saved runs stay in this session. Download your evidence before leaving or refreshing. The hosted experiment runs on your browser CPU and does not connect to an H100.
Research connections · 6 archive entries
These lessons adapt ideas and methods. The original sources state their own model, data and validation scope.
Stanford Guest Lectures: AP293 (Fall 2025) ↗
This entry reviews the article and lecture outlines, not a full transcription of the videos.
Belief Dynamics Reveal the Dual Nature of In-Context Learning and Activation Steering ↗
A fitted account of model behaviour does not establish that a model has human beliefs.
Priors in Time: Missing Inductive Biases for Language Model Interpretability ↗
Our Bayesian story model is explicit and hand-specified; it is not Temporal Feature Analysis applied to an LLM.
Reasoning Theater: Probing for Performative Chain-of-Thought ↗
Savings depend on task and paper version; the Goodfire post and later arXiv revision report different percentages. Our trajectories are simulated.
Meandering on Manifolds: The Neural Geometry of Stories Over Time ↗
Representing a character's emotions does not mean the model has emotions. Classroom story probabilities are hand-specified.
Under the Hood of a Reasoning Model ↗
An SAE reveals partial patterns, not a complete transcript of reasoning. Effects do not generalise automatically.