brightlabYear 12 · Seeded branching simulation · 55 minutes
Where does the answer fork?
When is an early confident answer premature?
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
Forking Fast: Efficiently Estimating Uncertainty Dynamics in Text Generation
Compare sampled branch probabilities with a known distribution as rollout count and smoothing change. Record the source model/task, a baseline, the changed factor, a measurement and an alternative explanation. State exactly which part your notebook investigates and which part it does not reproduce.
Our finite branching simulator is not a reasoning LLM. More samples reduce sampling noise, not model bias.
Use the notebook below to collect the measurements. This writing remains in this page session until downloaded.
Teacher background and source method
Llama-3-8B-Instruct and DeepSeek-R1-Distill-Llama-8B on tinyMMLU
Resamples continuations at shared prefixes and compares uncertainty estimates across sample budgets, spacing and smoothing.
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 · 3 archive entries
These lessons adapt ideas and methods. The original sources state their own model, data and validation scope.
Forking Fast: Efficiently Estimating Uncertainty Dynamics in Text Generation ↗
Our finite branching simulator is not a reasoning LLM. More samples reduce sampling noise, not model bias.
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.