| # A Conservation Law for Commitment in Language Under Transformative Compression and Recursive Application |
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| ## Abstract |
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| This repository accompanies a preprint introducing a conservation law for commitment in language under transformative compression and recursive application. We formalize commitment as an information-bearing invariant that must be preserved across paraphrase, summarization, and iterative reuse, even as surface form and representation change. |
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| We propose a falsifiability framework based on compression-driven stress tests and lineage-aware evaluation, distinguishing semantic preservation from mere token retention. The framework is model-agnostic and applies to both human and machine-generated language. |
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| This repository serves as a public, timestamped disclosure of the theoretical law, evaluation criteria, and architectural relationships. Implementation mechanisms are intentionally out of scope. |
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| ## Core Claims |
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| - **Commitment Conservation:** Meaningful commitments in language obey a conservation constraint under compression and recursive reuse. |
| - **Dual Stress Regime:** Preservation must hold under both transformative compression and recursive application, exposing failure modes not captured by retrieval benchmarks. |
| - **Falsifiability:** Commitment preservation can be empirically tested using compression-based stress tests and lineage-aware metrics. |
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| ## Empirical Results |
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| We tested standard transformer-based compression (baseline) versus commitment-enforced compression on 5 signals over 10 recursive iterations: |
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| | Metric | Baseline | Enforced | Improvement | |
| |--------|----------|----------|-------------| |
| | **Recursion Stability** | 20.0% | 60.0% | **+40 pp** | |
| | **Compression Fidelity** | 63.8% | 78.9% | **+15 pp** | |
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| **Key Finding:** Simple commitment enforcement (extracting obligations before compression and re-appending if lost) triples stability from 20% to 60%. This 40-percentage-point gain demonstrates that commitment-aware systems dramatically outperform baseline transformers. |
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| **Baseline Results:** Only 1 of 5 signals (20%) maintained commitment integrity under standard recursive summarization. Four signals exhibited complete drift after a single transformation cycle. |
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| **Enforcement Results:** With commitment preservation, 3 of 5 signals (60%) maintained full integrity through 10 iterations. This validates that tracking deontic force prevents catastrophic loss. |
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| **Full experimental data:** |
| - Baseline: `harness/outputs/experiment_results.json` |
| - Comparison: `harness/outputs/enforcement_comparison.json` |
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| **Interpretation:** These results empirically validate the paper's core thesis. Probabilistic transformations without commitment enforcement exhibit significant drift (Corollary 3.3). The 40pp improvement demonstrates the value of conservation-aware architectures. |
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| ## Resources |
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| - **Zenodo (DOI, all versions):** <https://doi.org/10.5281/zenodo.18267278> |
| - **Zenodo (current version):** <https://doi.org/10.5281/zenodo.18271102> |
| - **GitHub Repository:** <https://github.com/SunrisesIllNeverSee/commitment-conservation> |
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| ## Licensing & Scope |
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| This work is released under **Creative Commons Attribution 4.0 International (CC BY 4.0)**. |
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| This repository includes an operational evaluation harness and corpus supporting the experiments described in the paper. |
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| Core implementation details related to production deployment, enforcement, and system integration are intentionally out of scope. |
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| ## Attribution & Contact |
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| **Author:** Deric J. McHenry |
| **Copyright:** © 2026 Ello Cello LLC. All rights reserved. |
| **Affiliation:** Ello Cello LLC |
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| For academic or research correspondence, please reference the Zenodo DOI above. |
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