brightlabYear 6 · Explicit teaching model · 35 minutes
Around the year with Pip
How can a model reuse addition to reason about months?
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
A Geometric Calculator Inside a Neural Network
Add month numbers, then wrap around the calendar. 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.
The paper does not claim that Llama simply adds directly around a circle. Our calendar is a teaching model.
Use the notebook below to collect the measurements. This writing remains in this page session until downloaded.
Teacher background and source method
Llama 3.1 8B arithmetic and cyclic tasks
Tracks layer/token representations, identifies a shared addition mechanism and checks it with causal interventions.
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.
A Geometric Calculator Inside a Neural Network ↗
The paper does not claim that Llama simply adds directly around a circle. Our calendar is a teaching model.
Uncovering Neural Geometry in Vision Models With Block-Sparse Featurizers ↗
The experiments concern vision and diffusion models; the classroom geometry is an analogy for representation, not an LLM replication.
Steering Along Manifolds to Control Neural Networks ↗
Only selected fitted manifolds and tasks were tested; not every concept is circular.
The Neural Geometry Series ↗
This is an index, not an additional independent experiment.
Finding the Tree of Life in Evo 2 ↗
Biological geometry does not establish an equivalent map inside text LLMs.
The World Inside Neural Networks ↗
A cross-domain research perspective does not prove every useful concept has an easily readable geometry.