zacwhite/genui-compiler-corpus

GenUI Compiler Corpus

GenUI Compiler Corpus contains 5,000 accepted synthetic examples for training and evaluating a specialist that translates natural-language interface briefs into validated GenUI documents. GenUI is a model-facing JSON protocol rendered as native SwiftUI.

The corpus was created during OpenAI Build Week with Codex and GPT-5.6. It is groundwork for future fine-tuning and does not imply that the current GenUI demo ships with or depends on a fine-tuned model.

The project history and evidence trail are available in the GenUI Build Week devlog.

Composition

Task	Records
Description-to-UI generation	4,000
Diagnostic UI repair	1,000
Total	5,000

Split	Generation	Repair	Total
Train	3,373	880	4,253
Validation	360	97	457
Test	267	23	290

All records use a three-message chat format:

{
  "messages": [
    { "role": "system", "content": "..." },
    { "role": "user", "content": "..." },
    { "role": "assistant", "content": "..." }
  ]
}

The user message's content is a JSON string. Generation records use task: "compile" and include a brief, component catalog, and optional app-specific component descriptors. Repair records use task: "repair" and additionally include an invalid candidate plus structured diagnostics. The assistant content is exactly one canonical GenUI JSON document with no prose or code fence.

Creation and validation

GPT-5.6 generated candidate records and independently reviewed guarded checkpoints. Deterministic tooling—not model review alone—controlled final acceptance. The pipeline enforced:

• bounded decoding and GenUI document validation;
• exact task and split accounting;
• unique accepted training inputs and record hashes;
• mechanical component, state, binding, layout, repetition, and constraint requirements;
• semantic evidence tied to the requested target component;
• balanced coverage across ten compiler dimensions;
• separation of generation and repair records; and
• deterministic replay and verification after evaluator changes.

The final run contained exactly 4,000 generation and 1,000 repair records, 5,000 unique accepted record hashes, and passed final verification at checkpoint 50.

Release contents

This release contains only the accepted, redacted training exports:

• train.jsonl
• validation.jsonl
• test.jsonl
• manifest.json

It excludes raw model sessions, prompts and retry logs from orchestration, rejected candidates, render archives, Codex session IDs, detailed judge metadata, account identifiers, and paid-training configuration.

Intended uses

• Fine-tuning a specialist for GenUI description-to-document compilation and diagnostic repair.
• Evaluating structured-generation behavior across held-out GenUI scenarios.
• Researching deterministic validation and bounded repair around model-authored UI.

Always decode and validate generated documents with the current GenUI runtime. This dataset is not a substitute for runtime limits, validation, secure-data policies, or host ownership of actions and resources.

Limitations

• The corpus is fully synthetic and may reflect the biases and blind spots of its generators, reviewers, scenario templates, and deterministic gates.
• It targets the unversioned GenUI protocol and catalog snapshot identified in manifest.json; later runtime versions may not be compatible.
• Passing corpus acceptance does not guarantee that an example is ideal for every product, accessibility context, locale, or device.
• The train, validation, and test exports repeat the complete compiler instruction and component catalog inside each example for self-contained supervised fine-tuning.
• No fine-tuned model is included with this dataset.

License

The dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). You may share and adapt it, including commercially, provided you give appropriate credit, link the license, and indicate whether you made changes.

Suggested attribution:

GenUI Compiler Corpus by Zac White, licensed under CC BY 4.0.

Citation

@dataset{white_2026_genui_compiler_corpus,
  author = {White, Zac},
  title = {GenUI Compiler Corpus},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/zac/genui-compiler-corpus}
}