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Main Author: RS0136
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Published: Zenodo 2025
Online Access:https://doi.org/10.5281/zenodo.17078519
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author RS0136
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contents <h2>Summary</h2> <p>First stable release of the AIP Simulation Suite. This version generates all 14 paper figures (PNG) with matching LaTeX tables and CSV data, demonstrating the affine equivalence between Expected Free Energy (AIF/EFE) and pragmatic utility in RSA under assumptions A1–A6.</p> <h2>Highlights</h2> <ul> <li>Deterministic parallel execution (per-seed RNG + seed-sorted collation)</li> <li>14 figures + 14 paired LaTeX tables, ready to \input into your preprint</li> <li>Calibration, ablation (risk/ambiguity/IG), noise robustness, parameter recovery</li> <li>Model comparison (LL, BIC), ΔBIC-ready tables</li> <li>No seaborn, no custom colors; single-plot, publication-friendly figures</li> <li>Corpus used only to shape distributions; synthetic fallback if missing</li> </ul> <h2>Requirements</h2> <ul> <li>Python ≥ 3.9</li> <li>numpy, pandas, matplotlib, joblib</li> </ul> <h2>Install</h2> <p>pip install numpy pandas matplotlib joblib</p> <h2>Usage</h2> <p>python aip.py --input <folder-with-filteredCorpus.csv> --output <outdir> --cores <n> <br> [--trials-per-seed 1000] [--seeds 0-29]</p> <h2>Reproducibility</h2> <ul> <li>Default seeds: 0–29</li> <li>Results collated in deterministic seed order</li> <li>Verify stability by checksumming <code>output/data/per_seed_summary.csv</code> across repeated runs</li> </ul> <h2>Artifacts</h2> <ul> <li>output/figures/fig01.png ... fig14.png</li> <li>output/tables/fig01_table.tex ... fig14_table.tex</li> <li>output/data/*.csv, output/run_config.json</li> </ul> <h2>Notes</h2> <ul> <li>L0 uses probabilistic HSL semantics; bridge makes β ↔ 1/α explicit.</li> <li>If <code>filteredCorpus.csv</code> is absent, a small synthetic corpus is generated for smoke tests.</li> </ul> <h2>License</h2> <p>MIT</p>
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publishDate 2025
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spellingShingle RS0136/AIP: AIP Simulation Suite v1.0.0 — Reproducible Figures & LaTeX Tables for AIF ⇔ RSA
RS0136
<h2>Summary</h2> <p>First stable release of the AIP Simulation Suite. This version generates all 14 paper figures (PNG) with matching LaTeX tables and CSV data, demonstrating the affine equivalence between Expected Free Energy (AIF/EFE) and pragmatic utility in RSA under assumptions A1–A6.</p> <h2>Highlights</h2> <ul> <li>Deterministic parallel execution (per-seed RNG + seed-sorted collation)</li> <li>14 figures + 14 paired LaTeX tables, ready to \input into your preprint</li> <li>Calibration, ablation (risk/ambiguity/IG), noise robustness, parameter recovery</li> <li>Model comparison (LL, BIC), ΔBIC-ready tables</li> <li>No seaborn, no custom colors; single-plot, publication-friendly figures</li> <li>Corpus used only to shape distributions; synthetic fallback if missing</li> </ul> <h2>Requirements</h2> <ul> <li>Python ≥ 3.9</li> <li>numpy, pandas, matplotlib, joblib</li> </ul> <h2>Install</h2> <p>pip install numpy pandas matplotlib joblib</p> <h2>Usage</h2> <p>python aip.py --input <folder-with-filteredCorpus.csv> --output <outdir> --cores <n> <br> [--trials-per-seed 1000] [--seeds 0-29]</p> <h2>Reproducibility</h2> <ul> <li>Default seeds: 0–29</li> <li>Results collated in deterministic seed order</li> <li>Verify stability by checksumming <code>output/data/per_seed_summary.csv</code> across repeated runs</li> </ul> <h2>Artifacts</h2> <ul> <li>output/figures/fig01.png ... fig14.png</li> <li>output/tables/fig01_table.tex ... fig14_table.tex</li> <li>output/data/*.csv, output/run_config.json</li> </ul> <h2>Notes</h2> <ul> <li>L0 uses probabilistic HSL semantics; bridge makes β ↔ 1/α explicit.</li> <li>If <code>filteredCorpus.csv</code> is absent, a small synthetic corpus is generated for smoke tests.</li> </ul> <h2>License</h2> <p>MIT</p>
title RS0136/AIP: AIP Simulation Suite v1.0.0 — Reproducible Figures & LaTeX Tables for AIF ⇔ RSA
url https://doi.org/10.5281/zenodo.17078519