README.md

---
license: apache-2.0
language:
  - en
  - zh
  - tl
  - es
  - hi
tags:
  - multilingual
  - translation
  - world-models
  - egocentric-video
  - robotics
  - annotations
  - json-schema
pretty_name: UniversalLabeler 1.0
size_categories:
  - n<1K
---

# UniversalLabeler

**A loss-audited interchange format for multilingual world-model annotations.**

![Language surfaces converge into an evidence-linked semantic packet and expand into five languages.](assets/semantic-packet.png)

UniversalLabeler separates what happened from how a language describes it. A
source annotation is represented as small, evidence-linked claims—action,
participants, hand roles, objects, state change, place, time and outcome. Human
language captions and dataset-native labels are projections of the same packet,
with omissions recorded rather than hidden.

This is a public data and schema release. It does not include a translation
service, model prompts or private processing infrastructure.

## Release 1.0.5

| | |
|---|---:|
| Label-language profiles | 19 |
| Human languages | 5 |
| World-model dimensions | 22 |
| Typed annotation kinds | 13 |
| Controlled conformance frames | 6 |
| Language surfaces | 30 |
| License | Apache-2.0 |

Human-language profiles currently cover English, Simplified Chinese, Tagalog,
Spanish and experimental Hindi. The controlled examples are test fixtures, not
a general-purpose translation benchmark.

## Data model

```text
native record + evidence
          │
          ▼
┌─────────────────────────────────────────────┐
│ Universal Annotation Packet                │
│ action · roles · hands · objects · state   │
│ place · time · outcome · uncertainty       │
│ evidence selectors · provenance            │
└─────────────────────────────────────────────┘
          │
          ├── dataset-native label
          ├── English
          ├── 简体中文
          ├── Tagalog
          ├── Español
          └── हिन्दी
```

The packet is compositional rather than a fixed dictionary of every possible
verb and noun. Concepts receive stable identifiers; semantic roles describe
their relationship to an event; evidence and source-clock selectors bind each
claim to the underlying record. Language-specific grammar remains in the
projection layer.

## Repository contents

| Path | Purpose |
|---|---|
| `ontology/universal-annotation-core-v1.json` | Node types, predicates and availability dimensions |
| `data/label-languages-v1.json` | Dataset and human-language profiles |
| `data/world-model-label-audit-v1.json` | Cross-dataset supervision audit |
| `data/controlled-action-matrix.jsonl` | Five-language conformance fixtures |
| `schemas/` | Strict JSON Schemas for packets, projections and audits |
| `examples/fold-towel.uap.json` | Grounded event with hands, state, time and provenance |
| `examples/*.adapter.json` | Loss-audited dataset views |
| `rubrics/` | Bilingual review and evaluation protocol |
| `metadata/` | Reproducibility and validation records |

An interactive explanation is included at `demo/index.html`. Each of its four
first-person event images is a generated, synthetic visual preview—not upstream
evidence or a source-dataset frame.

## Recommended workflow

1. Pin the source dataset, schema revision and native record.
2. Preserve the native record and content digest.
3. Encode only evidence-supported atomic claims. Mark missing dimensions as
   not collected, not applicable or unknown.
4. Render every target language directly from the same claim packet; do not use
   one translated language as the source for the next.
5. Record represented and omitted claim IDs for every projection.
6. Validate against the included schemas.
7. Require independent bilingual and evidence-grounded review before accepting
   generated language as dataset annotation.

## Evaluation boundary

Release validation checks schema correctness, content addressing, ontology
references, semantic equivalence across the controlled matrix, private-data
markers and byte-identical rebuilds. Exact results and hashes are in
`metadata/validation.json` and `SHA256SUMS`.

These checks establish format conformance. They do not establish open-vocabulary
translation quality. Round trips can reproduce the same mistake twice;
production releases still require bilingual review against video or other
source evidence. The proposed acceptance thresholds and adversarial strata are
specified in `rubrics/EVALUATION_PROTOCOL.md`.

## Known limits

- The six controlled frames cover predicate–patient directives only.
- Chinese aspect and classifiers, Tagalog voice/pivot, Spanish morphology,
  Hindi agreement, code switching, negation and quantifier scope need broader
  reviewed data.
- The supervision audit identifies common world-model dimensions; it does not
  claim complete adapters for every upstream dataset.
- Synthetic receipt identifiers are not evidence of human review.

## License

UniversalLabeler's original schemas, profiles, ontology and synthetic fixtures
are released under Apache-2.0. Upstream datasets, media and annotations retain
their own licenses and access conditions and are not redistributed here.

## Citation

```bibtex
@dataset{universal_labeler_2026,
  author = {Pablo and contributors},
  title = {UniversalLabeler 1.0: Loss-Resistant Translation Contracts for World-Model Labels},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/itspublu/UniversalLabeler}
}
```