Applied Research on Generative Models
Clear, practical experiments on generative models.
Applied Models is a public lab for evidence-first work on generative models: validation, interpretability, post-training, model anatomy, and constrained implementation. Each release starts with one hypothesis, one focused experiment, and a clear record of what happened.
Operating Loop
- ChoosePick one model or setup to study.
- DefineSet one hypothesis and the key questions around it.
- RunExecute the smallest real experiment that produces evidence.
- PublishRecord the result, even if it is partial or failed.
Editorial Boundary
- No learning notesPublished work should not be passive study notes.
- No repostingDo not summarize other people's blogs or papers as output.
- Original onlyKeep the record tied to direct implementation and measurement.
- Keep movingProgress matters more than chasing state-of-the-art optics.
Artifact Index
Recent artifacts in one compact table.
Use this as the quick reference layer: scan the latest published work, then open the artifact you need.
| Type | Title | Reference | Meta | Action |
|---|---|---|---|---|
| Notebooks |
0001 Agentic Evals Baseline Notebook
Python notebook |
A Python notebook for prompt fixtures, scoring checks, and baseline observations for the first experiment. | 2026-02-28 · Python notebook | |
| Experiments |
Experiment 0001: Agentic Evals for Small Models
Eval suite |
A compact evaluation suite for planning, tool choice, self-correction, and distractor resistance in smaller open models. | 2026-02-28 · In progress · Eval suite | |
| Articles |
Operating Principles for Applied Models
Working note |
The project runs on small, hypothesis-driven experiments, original records of work, and continuous forward motion instead of SOTA chasing. | 2026-02-28 · Working note |