Commitment Conservation Harness
Open evaluation framework for testing the conservation law proposed in the preprint.
📄 Paper: A Conservation Law for Commitment in Language Under Transformative Compression (v0.03)
🏗️ Zenodo Record: https://zenodo.org/records/18274930
💻 GitHub: https://github.com/SunrisesIllNeverSee/commitment-conservation
Quick Start
Run the harness locally:
# Clone and install
git clone https://huggingface.co/burnmydays/commitment_conservation_harness
cd commitment_conservation_harness/harness
pip install -r requirements.txt
# Run baseline experiments
python run_experiments.py
# Run baseline vs enforced comparison
python compare_enforcement.py
Results saved to harness/outputs/ as JSON files showing baseline vs enforced stability.
A Conservation Law for Commitment in Language Under Transformative Compression and Recursive Application
Abstract
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.
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.
This repository serves as a public, timestamped disclosure of the theoretical law, evaluation criteria, and architectural relationships. Implementation mechanisms are intentionally out of scope.
Core Claims
- 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.
Empirical Results
We tested standard transformer-based compression (baseline) versus commitment-enforced compression on 5 signals over 10 recursive iterations:
| Metric | Baseline | Enforced | Improvement |
|---|---|---|---|
| Recursion Stability | 20.0% | 60.0% | +40 pp |
| Compression Fidelity | 63.8% | 78.9% | +15 pp |
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.
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.
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.
Full experimental data:
- Baseline:
harness/outputs/experiment_results.json - Comparison:
harness/outputs/enforcement_comparison.json
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.
Resources
- 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
Licensing & Scope
This work is released under Creative Commons Attribution 4.0 International (CC BY 4.0).
This repository includes an operational evaluation harness and corpus supporting the experiments described in the paper.
Core implementation details related to production deployment, enforcement, and system integration are intentionally out of scope.
Attribution & Contact
Author: Deric J. McHenry
Copyright: © 2026 Ello Cello LLC. All rights reserved.
Affiliation: Ello Cello LLC
For academic or research correspondence, please reference the Zenodo DOI above.
⚖️ Intellectual Property Notice
MO§ES™ (Modus Operandi §ignal Scaling Expansion System) is a trademark of Ello Cello LLC.
All systems, methods, and written material contained herein are protected under applicable copyright law and are part of one or more provisional patent applications filed with the United States Patent and Trademark Office (USPTO).
© 2026 Ello Cello LLC. All rights reserved.
Unauthorized use, duplication, or redistribution of any part of this communication — including proprietary system architecture or terminology — is strictly prohibited and may result in legal action.