README.md

metadata

title: PRML Playground
emoji: 🔐
colorFrom: green
colorTo: gray
sdk: static
pinned: false
license: mit
short_description: In-browser SHA-256 manifest editor for the PRML spec

PRML Playground

Edit a Pre-Registered ML Manifest in your browser. Watch canonical bytes update. SHA-256 computed via Web Crypto. Optionally anchor publicly.

This is a Hugging Face Space mirror of the playground at falsify.dev/playground. Same code, same hash output (byte-equivalent to the Python reference across 21 conformance vectors), zero server-side state.

Latest: The PRML JSON Schema was merged into the SchemaStore catalog on 2026-05-11 — *.prml.yaml files now autocomplete in VS Code, JetBrains, Helix, Zed, and Cursor out of the box. v0.1 is also published on Zenodo with a citable DOI: 10.5281/zenodo.20177839.

What is PRML?

PRML (Pre-Registered ML Manifest) is an open CC BY 4.0 specification for committing ML evaluation claims to a SHA-256 hash before the experiment runs. Eight YAML fields hashed over canonical bytes — the hash is the receipt that the threshold direction was fixed before the data arrived.

• Spec: spec.falsify.dev/v0.1
• v0.2 RFC (frozen 2026-05-22): spec.falsify.dev/v0.2-rfc
• JSON Schema: spec.falsify.dev/schema/
• Reference impls: four — pip install falsify (PyPI) · npm install falsify-js (npm) · Go + Rust source at studio-11-co/falsify/impl/ (all MIT, byte-equivalent across 21 conformance vectors)
• Repos: github.com/studio-11-co/falsify

How to use this Space

1. Pick one of the four pre-loaded examples (minimal accuracy, RLHF win-rate, streaming Elo, revoked) or paste your own YAML
2. Watch the canonical bytes and SHA-256 update on every keystroke
3. Click Anchor publicly to commit the hash to the public registry at registry.falsify.dev — no account, no server-side state beyond the hash and a timestamp
4. Or click Generate badge for a local-only badge URL

Pure browser computation. The SHA-256 is computed via the Web Crypto API (crypto.subtle.digest). No data leaves your browser unless you click "Anchor publicly".

Why pre-registration

Most published ML accuracy numbers are unfalsifiable in practice. The metric, threshold, dataset slice, and random seed are reported after the experiment, leaving no cryptographic record of what was committed before it.

PRML closes one specific gap: it makes post-hoc threshold tuning mechanically detectable. It is not an attestation framework, a benchmark, or a publication-integrity system. PRML §8.1 names what it does not solve: a publisher who pre-registers ten claims and publishes only the favourable ones is still operating below the line of public accountability.

License

• This Space (HTML/CSS/JS): MIT
• PRML specification: CC BY 4.0
• Patent non-assertion grant is published with the spec: implementations are free to use the manifest schema, canonical byte serialisation, and registry anchor protocol royalty-free, irrevocably, worldwide.

Cite

Öztürk, C. (2026). PRML: A Pre-Registered Manifest Format for Machine Learning Evaluation Claims (v0.1). Zenodo. https://doi.org/10.5281/zenodo.20177839

Authors

Cüneyt Öztürk. Contact: hello@falsify.dev · falsify.dev