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.
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:
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.