Blame for README.md

---
language:
- en
library_name: pytorch
license: other
license_name: mit-code-and-cc-by-4-0-research-artifacts
license_link: LICENSE.md
tags:
- bayesian-inference
- tabular-classification
- compositional-generalization
- safetensors
---

# Predictive representations for compact Bayesian decision making

Reproduction assets for *Predictive Representations for Compact Bayesian Decision Making: Controlled Compression and Compositional Sensor Diagnosis*.

Includes all 132 sensor-model checkpoints (11 methods, two label budgets, three seeds, two datasets), processed features and split indices, generated probability laws and samples, complete reference scores, fixed protocols and executable code. Checkpoints use paired JSON and Safetensors files. No pretrained language model or executable pickle checkpoint is required.

## Run

Python 3.11+ and a CUDA-enabled PyTorch installation are required. The recorded environment is PyTorch 2.6.0/CUDA 12.4 on an NVIDIA H200; timings depend on hardware.

```bash
python -m pip install -r requirements.txt
python reproduce.py --mode check --output checksum_report.json
python reproduce.py --mode all --device cuda:0 --output replay_report.json
python analyze.py --device cuda:0 --output statistics.json
python paper/build_figures.py
python postconfirmation.py --device cuda:0 --output postconfirmation_replay
```

Replay checks all 132 checkpoints, the controlled primary comparison and the four-day sign-flip result. Analysis recomputes paired uncertainty from the supplied reference scores. Figure generation writes all article figures and tables under paper/.

Optional independent refitting, source reconstruction and sensitivity checks:

```bash
python reproduce.py --mode refit --device cuda:0 --output refit_report.json
python rebuild_sources.py --device cuda:0 --cache raw_downloads --output source_rebuild.json
python rebuild_external.py --device cuda:0 --output external_reproduction
python rebuild_controlled.py --device cuda:0 --output controlled_reproduction
python paper/sensitivity_analysis.py --device cuda:0 --output sensitivity.json
```

Raw-source reconstruction downloads the official archives and verifies their digests. External and full controlled reruns can take substantially longer than checkpoint replay. Use a new output directory for each rerun. Original arrays and numerical modules are unchanged.

## Contents and scope

- models/: every fitted sensor comparison, including adverse results.
- data/: processed sensor inputs, labels, splits, generated laws and sample streams.
- results/: reference predictions, complete external metrics and controlled frontiers.
- protocols/ and provenance/: fixed recipes, source definitions, eligibility failures and measured compute.
- unionmind/: numerical implementations; use the standalone commands above.
- SHA256SUMS.json: integrity hashes for the distributed files.

The controlled compression gain is 0.1013 utility. The external compression improvement criterion fails. The four-day temporal comparison is secondary because one source day fails its clock check; exact one-sided day-level p=0.0625. The 128-label hydraulic reversal, calibration failure and lack of complete-cost superiority are retained. These artifacts do not establish operational root-cause reliability.

## Additional analysis on opened data

An action-value compression control, a complete 1,212-episode partition and normalization checks for all 132 checkpoints, and crossed frozen hydraulic means/covariances are in results/postconfirmation/. The single added encoder was specified after the original confirmation was opened. Its specification and code hash are in protocols/postconfirmation_analysis_01.json. Original protocols, promotion decisions and checkpoints are unchanged. The command above recomputes the complete analysis without refitting. Measured timings are hardware-dependent.

## Data and license

Code is MIT; original models, generated data and research outputs are CC BY 4.0. Hydraulic features derive from Helwig, Pignanelli and Schuetze (UCI DOI 10.24432/C5CW21); temperature-modulated sensor features derive from Burgues (10.24432/C5S302). Gas-drift recordings (Vergara, 10.24432/C5RP6W) are retrieved separately and are not redistributed; preserve the source's legacy research-only notice. Full attribution and terms are in LICENSE.md.

## Reviewer-revision experiments

`revision_r1/` adds exploratory mechanism tests on the already opened data:
full controlled action-value/loss comparisons, external sample-support and
fixed-policy transfer comparisons, and 318 sensor fits with all seeds and
budgets retained. Original results, protocols and promotion decisions are unchanged.
The 27 sensor state bundles use only Safetensors and JSON. Specifications,
all comparative scores, cost records and numerical tie-sensitivity diagnostics
are included; these are additional analyses, not new confirmation.

Replay every added sensor state without fitting:

```bash
python revision_r1/sensor/verify_all.py --release . --base revision_r1/sensor/run_01 --extension revision_r1/sensor/objective_extension_01 --output revision_sensor_replay.json --device cuda:0
```

Recompute the controlled interventions in a new directory:

```bash
python revision_r1/controlled/run.py --prepare --release . --output new_controlled --device cuda:0
python revision_r1/controlled/run.py --release . --output new_controlled --device cuda:0
```

The external analysis requires separately acquired gas-drift recordings. Retain
the provider's research-use notice; no transformed gas records are redistributed.

```bash
python rebuild_external.py --output new_external_source --device cuda:0
python revision_r1/external/run.py specify --release . --input new_external_source --output new_external_analysis --device cuda:0
python revision_r1/external/run.py run --release . --input new_external_source --output new_external_analysis --device cuda:0
```

Sensor refitting is optional through `revision_r1/sensor/review_r1_sensor.py`
and `objective_extension.py`, each accepting `--release .`, `--output` and
`--device`. Use a new output directory. Rank gains do not establish calibrated
acceptance, and sparse fitted-law ties can affect ancillary realized utilities;
the unchanged primary mixture frontiers and all disclosed discrepancies remain available.

## Additional scientific validation

`validation_20261110/` contains the locked new-sensor replication, strict nested
source calibration and optimized Gaussian inference comparison. The new sensor
test passes support checks but fails the preselected neural improvement criterion.
All methods, budgets and negative results are retained. Original artifacts remain
unchanged. [Reproduction commands and source terms](validation_20261110/README.md)
include the separately acquired Air Quality archive; no restricted raw or
processed recordings are redistributed. Portable reruns reproduce an existing
study and do not constitute new prospective confirmation.