Publish seven audited Open-Jev data configs with frozen provenance

Includes two documented redistributable mixture projections, five prepared control corpora, exact raw records, Parquet splits, original manifests and original-mixture restoration. Excludes Wikispeedia task payload and external evaluation payload.

LICENSE-CODE-MIT

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+MIT License
+
+Copyright (c) 2026 Open-Jev contributors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

LICENSE-DATA

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+Original generated Open-Jev records are dedicated under CC0 1.0 Universal.
+https://creativecommons.org/publicdomain/zero/1.0/legalcode
+This dedication does not relicense upstream wording, sources, game assets, model weights, or code. See README.md and THIRD_PARTY_NOTICES.md.

README.md

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+---
+license: cc0-1.0
+language:
+  - en
+  - zh
+task_categories:
+  - text-classification
+tags:
+  - open-jev
+  - synthetic
+  - typed-decisions
+  - probability-estimation
+  - control
+size_categories:
+  - 100K<n<1M
+configs:
+  - config_name: release-v2-redistributable
+    default: true
+    data_files:
+      - split: train
+        path: data/release-v2-redistributable/train-*.parquet
+      - split: calibration
+        path: data/release-v2-redistributable/calibration-*.parquet
+      - split: validation
+        path: data/release-v2-redistributable/validation-*.parquet
+      - split: test
+        path: data/release-v2-redistributable/test-*.parquet
+      - split: ood
+        path: data/release-v2-redistributable/ood-*.parquet
+  - config_name: browser-drone-expansion-v1-redistributable
+    data_files:
+      - split: train
+        path: data/browser-drone-expansion-v1-redistributable/train-*.parquet
+      - split: calibration
+        path: data/browser-drone-expansion-v1-redistributable/calibration-*.parquet
+      - split: validation
+        path: data/browser-drone-expansion-v1-redistributable/validation-*.parquet
+      - split: test
+        path: data/browser-drone-expansion-v1-redistributable/test-*.parquet
+      - split: ood
+        path: data/browser-drone-expansion-v1-redistributable/ood-*.parquet
+  - config_name: citation-control-v1
+    data_files:
+      - split: train
+        path: data/citation-control-v1/train-*.parquet
+      - split: calibration
+        path: data/citation-control-v1/calibration-*.parquet
+      - split: validation
+        path: data/citation-control-v1/validation-*.parquet
+      - split: test
+        path: data/citation-control-v1/test-*.parquet
+      - split: ood
+        path: data/citation-control-v1/ood-*.parquet
+  - config_name: entity-alignment-control-v1
+    data_files:
+      - split: train
+        path: data/entity-alignment-control-v1/train-*.parquet
+      - split: calibration
+        path: data/entity-alignment-control-v1/calibration-*.parquet
+      - split: validation
+        path: data/entity-alignment-control-v1/validation-*.parquet
+      - split: test
+        path: data/entity-alignment-control-v1/test-*.parquet
+      - split: ood
+        path: data/entity-alignment-control-v1/ood-*.parquet
+  - config_name: amount-extraction-control-v1
+    data_files:
+      - split: train
+        path: data/amount-extraction-control-v1/train-*.parquet
+      - split: calibration
+        path: data/amount-extraction-control-v1/calibration-*.parquet
+      - split: validation
+        path: data/amount-extraction-control-v1/validation-*.parquet
+      - split: test
+        path: data/amount-extraction-control-v1/test-*.parquet
+      - split: ood
+        path: data/amount-extraction-control-v1/ood-*.parquet
+  - config_name: email-selection-control-v1
+    data_files:
+      - split: train
+        path: data/email-selection-control-v1/train-*.parquet
+      - split: calibration
+        path: data/email-selection-control-v1/calibration-*.parquet
+      - split: validation
+        path: data/email-selection-control-v1/validation-*.parquet
+      - split: test
+        path: data/email-selection-control-v1/test-*.parquet
+      - split: ood
+        path: data/email-selection-control-v1/ood-*.parquet
+  - config_name: phone-extraction-control-v1
+    data_files:
+      - split: train
+        path: data/phone-extraction-control-v1/train-*.parquet
+      - split: calibration
+        path: data/phone-extraction-control-v1/calibration-*.parquet
+      - split: validation
+        path: data/phone-extraction-control-v1/validation-*.parquet
+      - split: test
+        path: data/phone-extraction-control-v1/test-*.parquet
+      - split: ood
+        path: data/phone-extraction-control-v1/ood-*.parquet
+---
+
+# Open-Jev: typed decision datasets
+
+Open-Jev turns a state and a question into a typed decision: a yes/no probability, a distribution over choices, independent label probabilities, or a discrete numeric/ordinal decision. This repository publishes seven separate, frozen data configs from the [Open-Jev project](https://github.com/Zefan-Cai/Open-Jev-Dev), together with original manifests, exact raw records, source code and reconstruction instructions.
+
+These are controlled, mostly synthetic tasks and reference labels. They are not official TypeSafe/Jev training data, model predictions, or evidence of general capability. Open-Jev is independently implemented and is not affiliated with TypeSafe.
+
+## Configs and exact split counts
+
+| Config | Train | Calibration | Validation | Test | OOD | Total |
+|---|---:|---:|---:|---:|---:|---:|
+| `release-v2-redistributable` | 79,116 | 4,672 | 3,723 | 10,356 | 15,701 | 113,568 |
+| `browser-drone-expansion-v1-redistributable` | 108,624 | 6,794 | 5,493 | 14,726 | 25,160 | 160,797 |
+| `citation-control-v1` | 2,520 | 200 | 180 | 300 | 800 | 4,000 |
+| `entity-alignment-control-v1` | 6,944 | 728 | 280 | 1,008 | 2,240 | 11,200 |
+| `amount-extraction-control-v1` | 32,984 | 2,232 | 992 | 3,472 | 9,920 | 49,600 |
+| `email-selection-control-v1` | 3,618 | 81 | 189 | 432 | 1,080 | 5,400 |
+| `phone-extraction-control-v1` | 12,350 | 855 | 380 | 1,615 | 3,800 | 19,000 |
+
+**The configs overlap.** `release-v2-redistributable` is contained in `browser-drone-expansion-v1-redistributable`; do not add config totals and call them unique examples. Counts are typed decision rows. Several heads may come from the same conversation, document, family or game trajectory.
+
+The original frozen `release-v2` used to train the 2B/9B models has **80,816 training rows** and **115,821 rows across all splits**. The public projection above is not that exact training dataset. Each of the two composite configs excludes exactly **2,253 Wikispeedia rows**: 1,700 train, 89 calibration, 69 validation, 176 test and 219 OOD. The original expansion mixture has 163,050 rows, including 110,324 train. Original manifests and hashes are preserved without modification; [REPRODUCTION.md](REPRODUCTION.md) explains how to restore both exact original mixtures with separately obtained source data.
+
+The five citation/entity/amount/email/phone corpora were prepared and audited after those 2B/9B runs. **They have not been used for training or actual model inference at the time of this release.** The expansion mixture belongs to a separate 27B experiment; this dataset publication makes no completed-training or performance claim for that experiment.
+
+## Load and decode
+
+```python
+import json
+from datasets import load_dataset
+
+ds = load_dataset("ZefanCai/Open-Jev", "release-v2-redistributable")
+example = ds["train"][0]
+state = json.loads(example["state_json"])
+metadata = json.loads(example["metadata_json"])
+original_record = json.loads(example["record_json"])
+```
+
+For reproducibility, pass `revision="<dataset commit SHA>"`. Choose a config explicitly; loading the default does not include the five newly prepared corpora.
+
+| Column | Meaning |
+|---|---|
+| `id`, `group_id`, `split`, `source` | Original identity, grouping, split and generator/source version. |
+| `kind` | Original decision type; interpret with the source task definition. |
+| `question`, `options` | Model-visible question and ordered answer space. |
+| `target` | Original numeric reference targets, represented as a float64 list. These are labels, not measured model confidence. |
+| `state_json` | JSON encoding of the original state. Decoding returns a string or structured object, depending on the source. |
+| `metadata_json` | Original provenance and audit metadata. It can include privileged teacher/control labels and must not be used as model input. |
+| `record_json` | Complete original JSON record, retaining original object key order and numeric representation. |
+| `original_line_number` | One-based row position in the original frozen split, including positions of excluded rows. |
+
+The Parquet representation avoids imposing one nested schema on different task states. `raw/<config>/<split>.jsonl.gz` preserves the source JSONL bytes after decompression. For the filtered composites, retained lines preserve exact bytes and order. For the five other configs, decompressed files match the original frozen split hashes exactly.
+
+Use only `state`, `question`, `kind` and `options` as model inputs. Do not expose `target`, `metadata`, identities, split assignments or provenance fields to the model. Distribution, binary, multilabel and ordinal targets have different semantics; do not reduce every row to a single-class accuracy calculation.
+
+## Domains and construction
+
+- The base release covers controlled customer-support routing and triage; local workflow decisions; geometric painting probability requests; Snake and tic-tac-toe; simplified T-Rex/runner and platformer controls; numeric ViZDoom Basic trajectories; and controlled reasoning decisions.
+- The expansion additionally includes controlled browser state/action and drone state/control examples. These represent the declared simulated task forms, not unrestricted browser use or real aircraft operation.
+- Citation data uses original policy documents, quotes, visible facts and claims, with supported/contradicted/insufficient decisions. It tests those controlled relations, not arbitrary factual verification.
+- Entity alignment uses original catalog families, records, aliases and visible matching policies with multiple decision heads.
+- Amount, email and phone extraction separate deterministic candidate generation from typed selection/attribute decisions. Known candidate misses and partial matches are retained rather than replaced using gold answers. Conditional attribute heads and omitted-supervision counts are documented in the original manifests.
+
+The new corpora include compressed documents/families/cases in `artifacts/`. These are reproduction/audit artifacts, not additional typed rows to add to the totals. Citation has 200 documents and 4,400 cases: 4,000 typed semantic cases plus 400 quote-not-found controls. Entity alignment has 200 families and 2,800 cases; amount has 200 families and 3,200 documents; email has 200 families and 2,800 documents; phone has 200 families and 4,000 documents.
+
+Original data and split policies are recorded per corpus in `provenance/original-manifests/`. Related documents/entities/trajectories remain grouped within splits. OOD is source-specific, commonly reserved wording, layouts, control families or goals, and is not a universal unseen-domain benchmark. Local export verification checks unique IDs and cross-split group separation within each config.
+
+## Evaluation boundaries
+
+Train, calibration, validation, test and OOD are published separately. Train on the train split; use calibration only for the declared calibration procedure and validation for model selection. Test/OOD labels are public, so future work must disclose any use of them for development. Scores measured on the original full frozen mixtures must not be described as scores on these smaller public projections without recomputation.
+
+No Jev Frontier 100 question/answer payload is included. The external [jev-frontier-100 benchmark](https://github.com/softpudding/jev-frontier-100) remains separate from training and generation. No official private examples, game ROMs, game assets, model weights or credentials are included.
+
+## Licensing and provenance
+
+Original generated records are marked **CC0-1.0** in their existing provenance. This dedication covers our generated content, not upstream wording, external assets, source data or model weights. Original source code is **MIT**. Customer-control provenance retains its original note that short upstream question descriptions come from TypeSafe documentation without a verified source license; this release does not relicense those descriptions. See [THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md).
+
+The [Wikispeedia archive](https://snap.stanford.edu/data/wikispeedia.html) does not declare a verified separate graph/path redistribution license. Therefore its task rows are excluded from the two public mixture projections. We do not infer that a current Wikipedia license covers the archived graph/path dataset. We publish its original manifest, source URL, archive SHA-256, exact exclusion positions and a local reconstruction utility, not its graph, paths or task payload.
+
+Wikispeedia references:
+
+- West and Leskovec. *Human Wayfinding in Information Networks.* WWW 2012.
+- West, Pineau and Precup. *Wikispeedia: An Online Game for Inferring Semantic Distances between Concepts.* IJCAI 2009.
+
+`export-manifest.json` records original and public split counts/hashes, source counts, exclusion positions, source-code fingerprints and published file hashes. [REPRODUCTION.md](REPRODUCTION.md) documents exact restoration and generator commands. The dataset repository's Git commit pins this complete release.

REPRODUCTION.md

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+# Reproduction and original training-set restoration
+
+Download a pinned dataset revision before reproducing an experiment:
+
+```python
+from huggingface_hub import snapshot_download
+snapshot_download("ZefanCai/Open-Jev", repo_type="dataset",
+                  revision="<dataset commit SHA>", local_dir="open-jev-data")
+```
+
+`export-manifest.json` identifies every public artifact by SHA-256. Original frozen manifests are preserved unchanged in `provenance/original-manifests/`. Source code used during export is in `reproduce/source-code/` with file hashes in the export manifest; this snapshot is authoritative if the linked GitHub branch changes.
+
+## Exact published raw files
+
+Decompress `raw/<config>/<split>.jsonl.gz` to obtain native Open-Jev JSONL. For the five new configs the uncompressed SHA equals the original manifest SHA. For the two composite projections it equals `raw_uncompressed_sha256` in the export manifest, because only Wikispeedia rows are missing. Gzip encoding uses `mtime=0` and no embedded filename.
+
+Parquet retains the same records in `record_json`, and exposes structured top-level columns plus `state_json`/`metadata_json`. Parse those JSON columns before passing examples to the original code. Do not use audit metadata as model input.
+
+## Restore the exact original release-v2 and expansion mixtures
+
+The original `release-v2` includes 115,821 rows, and the original `browser-drone-expansion-v1` includes 163,050 rows. Both contain the same 2,253 Wikispeedia rows. Obtain those source data separately under their upstream terms:
+
+- Landing page: https://snap.stanford.edu/data/wikispeedia.html
+- Archive: https://snap.stanford.edu/data/wikispeedia/wikispeedia_paths-and-graph.tar.gz
+- Archive SHA-256: `97697096f5d2dcb77aa69e3992305c6c561de89edb9fb10b5ad9feaf8ba534d5`
+
+From `open-jev-data/reproduce/source-code`, with Python 3.10 or newer:
+
+```bash
+python -m jev.case_wikiracing --output-dir ../../../separately-obtained-wikiracing \
+  --targets 300 --pairs-per-target 12 --max-candidates 12 --seed 42
+```
+
+The generator fetches and verifies the fixed upstream archive. To use an existing archive, pass `--archive /path/to/wikispeedia_paths-and-graph.tar.gz` with the same SHA. This operation does not download any data from the public projections or modify a training directory.
+
+From the directory containing `open-jev-data` and `separately-obtained-wikiracing`:
+
+```bash
+python open-jev-data/reproduce/restore_original_mixture.py \
+  --release-root open-jev-data --config release-v2-redistributable \
+  --wiki-dir separately-obtained-wikiracing --output-dir restored-release-v2
+python open-jev-data/reproduce/restore_original_mixture.py \
+  --release-root open-jev-data --config browser-drone-expansion-v1-redistributable \
+  --wiki-dir separately-obtained-wikiracing --output-dir restored-browser-drone-expansion-v1
+```
+
+The utility checks every separate Wiki split against the original import manifest. It inserts Wiki rows at the recorded original positions, using exactly `jev.mix_data`'s JSON serialization, then verifies all ten restored split files against the original mixture hashes. It refuses to overwrite existing split files. This complete restoration was verified against the original frozen files during publication.
+
+Excluded row counts in each mixture are: train 1,700; calibration 89; validation 69; test 176; OOD 219. Their positions, but not their question/answer payload, are in `export-manifest.json`.
+
+## Original build commands
+
+The commands below document the original generator settings. Run in a separate workspace using the bundled source snapshot. Downloading/restoring the published frozen raw artifacts is the strongest byte-for-byte reproduction path; game generation additionally depends on its pinned runtime. The five newer manifests pin the relevant Python source hashes explicitly.
+
+Base mixture:
+
+```bash
+python -m jev.case_customer --output-dir data/case-customer --groups 1000
+python -m jev.case_workflows build --output-dir data/workflows-v1 --groups-per-workflow 250 --seed 42
+python -m jev.game_cli build-data all --output-dir data/games-v1 --episodes 100 --max-steps 80 --seed 42
+python -m jev.painting --output-dir data/painting-geometry-v1 --groups 60 --seed 42
+python -m jev.game_cli build-control-data --output-dir data/control-games-v1 --episodes 100 --max-steps 40 --seed 42
+python -m pip install vizdoom==1.2.4
+python -m jev.case_doom build --output-dir data/doom-basic-v1 --episodes 400 --ood-episodes 80 --seed 190919
+python -m jev.case_wikiracing --output-dir data/wikiracing --targets 300 --pairs-per-target 12 --max-candidates 12 --seed 42
+python -m jev.mix_data --inputs data/case-customer data/doom-basic-v1 data/wikiracing \
+  data/workflows-v1 data/painting-geometry-v1 data/games-v1 data/control-games-v1 --output-dir data/release-v1
+python -m jev.case_reasoning --output-dir data/reasoning-control-v1 --groups 2500 --seed 76109
+python -m jev.mix_data --inputs data/release-v1 data/reasoning-control-v1 --output-dir data/release-v2
+```
+
+Expansion:
+
+```bash
+python -m jev.case_browser --output-dir data/browser-v1 --groups 1000 --ood-groups 200 --seed 42
+python -m jev.case_drone --output-dir data/drone-control-v1 --groups 500 --ood-groups 100 --seed 42
+python -m jev.mix_data --inputs data/release-v2 data/browser-v1 data/drone-control-v1 \
+  --output-dir data/browser-drone-expansion-v1
+```
+
+Separately prepared new corpora:
+
+```bash
+python -m jev.case_citation --output-dir data/citation-control-v1 --groups 200 --ood-groups 40 --seed 42
+python -m jev.case_entity_alignment --output-dir data/entity-alignment-control-v1 --groups 200 --ood-groups 40 --seed 42
+python -m jev.case_amount_extraction --output-dir data/amount-extraction-control-v1 --groups 200 --ood-groups 40 --seed 42
+python -m jev.case_email_selection --output-dir data/email-selection-control-v1 --groups 200 --ood-groups 40 --seed 42
+python -m pip install phonenumbers==9.0.14
+python -m jev.case_phone_extraction --output-dir data/phone-extraction-control-v1 --groups 200 --ood-groups 40 --seed 42
+```
+
+This does not retrain any model. Check all generated files against the original manifest hashes before using them as a reproduction of a frozen corpus. Auxiliary case/document/family files in `artifacts/` can be decompressed directly; their uncompressed hashes are also recorded.
+
+## Rebuild this export from the frozen project workspace
+
+The GitHub repository keeps the export script and its input documentation in `reports/huggingface-data-release/`. With frozen source corpora available under that repository's `data/`, install `pyarrow`, then run:
+
+```bash
+python reports/huggingface-data-release/build_release.py \
+  --repo /path/to/Open-Jev-Dev --output /path/to/fresh-staging-directory
+```
+
+The exporter checks every frozen input split hash, applies the single documented source filter, validates all Parquet/raw round-trips and verifies both full-mixture restorations. Its allowlist excludes external benchmark payload, data drafts, game assets, credentials and model weights.

THIRD_PARTY_NOTICES.md

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+# Third-party attribution and release boundaries
+
+Open-Jev is independently implemented and is not affiliated with TypeSafe.
+
+- TypeSafe Jev/System One documentation and the public Jev launch inspired the task forms. Links and pinned source revisions are in `docs/public-capabilities.md`. No proprietary RLCD code, weights or private training dataset is included. Publicly viewable evaluation examples have not been assigned a verified general redistribution/training license and are not copied into our training data.
+- `achimala/jev-paint` (previously `jevinci`), copyright Anshu Chimala, MIT, inspired the four pixel probability representations. Our request builders, simple mean-color renderer and geometry generator are independently written. We do not bundle its impasto renderer or recorded model-probability fixtures.
+- Community projects are attributed in `docs/games.md` and `docs/community.md`. External game code/ROMs/assets are not bundled. Our grid, runner and platformer engines are original simplified environments.
+- Qwen model weights and tokenizer files retain the terms of their exact source repositories. The three pinned revisions were each verified as Apache-2.0; [model provenance](docs/model-provenance.md) records the fixed sources, content hashes and attribution. Code licensing here does not relicense weights. Publication of adapters must include the applicable license text, modification notices and a model card referencing the corresponding base revision.
+- The exact common Qwen [Apache-2.0 license](third_party/qwen/LICENSE) and [attribution/modification notice](third_party/qwen/README.md) are included for checkpoint packaging. Inference weight bundles use Apache-2.0 and include the repository's MIT source-code license separately; generated-data CC0 declarations do not apply to weights.
+- ViZDoom and its bundled scenario assets retain their upstream per-file licenses. They are optional installed dependencies; see `docs/doom-case.md`.
+- The Wikispeedia graph and BoolQ auxiliary data retain their source licenses and attribution, recorded by import manifests. They are fetched by data scripts rather than copied into Git.
+- Generated geometry, controlled conversations, local game states and local workflow records are marked CC0-1.0 in their provenance. This covers our generated records, not an upstream source or model.
+
+The repository license applies to original code only. Check the manifests and source documentation before redistributing optional data, engines, checkpoints or assets.

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