1. Submit the article
A contributor uploads a PDF, pastes the article text, adds a title, author and honest maturity label.
What is Hypothica for?
Hypothica is a platform for structured scientific hypotheses, mathematical models and theoretical frameworks. Its long-term goal is to become a machine-readable theory library: a place where human ideas can be reviewed, decomposed into explicit assumptions and equations, and then used as high-quality input for AI-assisted scientific reasoning.
Most early scientific ideas are hidden inside prose. Hypothica keeps the article and PDF, but adds a second layer: the Hypothica Model Graph. This graph separates a model into assumptions, definitions, equations, claims, predictions, falsification criteria, weak points and dependencies. That makes it easier to see what a theory actually says, what it depends on, and where it can be improved.
The Hypothica workflow
2. Review before publication
An admin checks whether the submission is serious, transparent and suitable for the public library.
3. Build the Model Graph
With AI assistance, the admin can convert the article into structured graph objects and import them into the database.
4. Improve the model
Once assumptions and dependencies are visible, people and AI can test consistency, compare theories and identify the next research steps.
Use Hypothica to
- publish early scientific ideas with visible maturity labels;
- store articles, PDFs, references, claims and model graphs in one place;
- make assumptions and weak points explicit instead of hiding them in prose;
- prepare better preprints, simulations, research proposals or follow-up theory work;
- create structured inputs that AI can reason over without confusing speculation with proof.
What Hypothica does not claim
Hypothica does not turn speculation into verified science. Publication is not peer review, and an AI-generated analysis is not evidence. The value of Hypothica is structure: it helps ideas become clearer, more falsifiable and easier to criticize.
The strongest submissions are not necessarily the most spectacular ones. They are the ones that clearly state their assumptions, equations, limitations and possible tests.