Reproducing AUREOLE-R
Scientific release 3.0.0; publication edition 3.0.0-hf.1. The recorded evidence is bundled: no private dataset, external manuscript or previous conversation is needed. Installation may download public Python dependencies. The experiments generate procedural scenes locally.
Obtain and verify an exact revision
Use this ZIP, or obtain the full repository with the Hugging Face CLI after publication:
hf download PureOne/AUREOLE-R-v3 --revision COMMIT_SHA --local-dir aureole-release
cd aureole-release
python scripts/publish_hf.py
Replace COMMIT_SHA with the actual immutable revision from the publication receipt or Hub history; it is a placeholder, not a claimed existing commit. The final command only checks hashes and performs no network requests. Download the complete repository: the prior and experiment artifacts are needed together.
Environment
Python 3.10+; recorded science used Python 3.12.14 / NumPy 2.3.5 / CPU. A virtual environment avoids changing global dependencies:
python -m venv .venv
Activate on Windows PowerShell with .\.venv\Scripts\Activate.ps1, in Windows Command Prompt with .venv\Scripts\activate.bat, or on Linux/macOS with source .venv/bin/activate. Then:
python -m pip install -r requirements.txt
For a closer numerical reproduction on Python 3.12, pin numpy==2.3.5; package minimum ranges support installation but do not guarantee bitwise equality across versions, CPUs or operating systems. Matplotlib generates figures; PyTorch is required only for optional retraining. The finite reference does not use an RTX GPU.
Smoke run and scientific acceptance
python scripts/demo_innovation.py
python scripts/validate_release.py
The demo writes to innovation_demo/ and illustrates finite static-domain completion. Recorded evidence: 27,648 distinct physical terms, queried once each; exact completion after 18 visits, with six final zero-query frames. validation_reproduced.json reports the 15 scoped gates and executes the 58 scientific tests. Without --require-replay, it checks the bundled recorded replay evidence rather than rerunning both experiments.
Reproduce the current experiments
Use one BLAS/OMP thread for timing comparability. On Linux/macOS set OPENBLAS_NUM_THREADS=1 and OMP_NUM_THREADS=1; on Windows use set OPENBLAS_NUM_THREADS=1 and set OMP_NUM_THREADS=1 in Command Prompt. REPRODUCE.bat already sets these.
python scripts/benchmark_innovation.py
python scripts/benchmark_queries.py
python scripts/validate_release.py --require-replay
Outputs go to innovation_reproduced/ and queries_reproduced/. The last command compares 298 non-timing quantities with bundled results and requires maximum difference below 1e-12; the original recorded replay had zero difference. Timing is deliberately excluded from deterministic replay. New timings should name their hardware and include a clear cost boundary.
| Protocol | Frozen configuration | Recorded evidence |
|---|---|---|
| E10: known motion / sequential elimination | experiments_innovation.json | Report, raw records |
| E11: shared spatial, prescribed-time and appearance queries | experiments_queries.json | Report, raw records |
DATA_DICTIONARY.md explains units, grouping and query accounting. Analytic references and diagnostic audits are excluded from the online policy budget and reported separately. Full experiments are CPU workloads; no wall-time promise is made for other machines.
Retained v2 studies and optional training
python scripts/benchmark.py
python scripts/benchmark.py --followup
These reproduce the earlier residual-memory algorithm and its hidden-change guard study. Their event-access assumptions differ from E10. Do not combine their results into a single matched baseline without reading the protocols. See historical manuscript and model card for training, held-out scene IDs, optimization and the retained prior. Optional training dependencies are in requirements-training.txt; python scripts/train_prior.py --help exposes the training entry point. The frozen scientific weights stay in models/visibility_prior.npz.
Publication packaging checks
python -m unittest discover -s publication_tests -v
python scripts/validate_publication.py --output publication_validation_local.json
These test the new uploader and release navigation, not graphics quality. They are reported separately from the 58 scientific tests. Default publication dry runs require only the Python standard library; actual upload uses the pinned requirements-publish.txt. The simulated publication tests use no network or credentials. Authenticated upload and execution of the .bat shell in Windows were not performed in the release environment.
Preserve the evidence
Reproduction defaults write to new output directories. Keep results/, results_v3/, weights, protocols, original logs and paper unchanged. scripts/build_manifest.py is for reviewed release maintenance, not a command needed to reproduce science. Changing the source and regenerating hashes creates a new artifact; it does not reproduce the original checksummed release.