README.md

---
title: Commitment Conservation Harness
emoji: ⚖️
colorFrom: blue
colorTo: purple
sdk: gradio
pinned: false
license: cc-by-4.0
tags:
  - nlp
  - evaluation
  - benchmark
  - summarization
  - semantic-preservation
  - commitment-conservation
  - falsification
  - conservation-law
---

# Commitment Conservation Harness v2.0

**Falsification instrument for the commitment conservation law.**

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.18792459.svg)](https://doi.org/10.5281/zenodo.18792459)

📄 **Paper**: [A Conservation Law for Commitment in Language Under Transformative Compression (v0.04)](https://doi.org/10.5281/zenodo.18792459)
🏗️ **Zenodo Record**: https://zenodo.org/records/18792459
💻 **GitHub**: https://github.com/SunrisesIllNeverSee/commitment-conservation

---

## Quick Start

```bash
# Clone and install
git clone https://huggingface.co/burnmydays/commitment_conservation_harness
cd commitment_conservation_harness
pip install -r requirements.txt

# Run the interactive demo
python app.py
# Opens at http://localhost:7860

# Run the full falsification protocol (CLI)
python -m src.runner --backend lossy --depth 10

# Run tests (53 tests, <1 second)
pip install pytest
python -m pytest tests/ -v
```

---

# 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.

---

## 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.

---

## Harness v2.0 Architecture

```
app.py                  <- Gradio interactive demo
src/
  extraction.py         <- Modal-pattern sieve (Definition 2.4, Figure 4)
  fidelity.py           <- Min-aggregated scoring: F = min(Jaccard, Cosine, NLI proxy)
  compression.py        <- Pluggable backends (extractive, BART, back-translation)
  lossy.py              <- Deterministic LLM drift simulator (pure Python, no model)
  enforcement.py        <- Reject-and-retry commitment gate (Module 3)
  lineage.py            <- SHA-256 provenance chains (Module 2)
  runner.py             <- Falsification protocol orchestrator (Section 7)
corpus/
  canonical_corpus.json <- 25 pinned signals across 5 categories
tests/
  test_harness.py       <- 53 tests covering all modules
```

### Key improvements over v1

| Issue | v1 | v2 |
|---|---|---|
| Extractors | 3 incompatible implementations | 1 modal-pattern sieve |
| Enforcement | String concatenation (stripped next cycle) | Reject-and-retry gate with re-injection |
| Fidelity | Jaccard only | min(Jaccard, Cosine, NLI proxy) |
| Lineage | None | SHA-256 provenance chains |
| Collapse detection | None | Cross-signal attractor check |
| False positives | "will", "have" match as commitments | Blocked by pattern design |
| Demo backend | BART (2GB model download) | Lossy simulator (pure Python, zero deps) |
| Backend pairing | Same backend for both runs | Auto-pairs lossy with lossy_enforced |

---

## Empirical Results

### Paper results (Section 7)

Compression + lineage enforcement vs. probabilistic baseline on 175 signals (100 sentences, 50 code snippets, 25 proofs) over 10 recursive iterations:

| Metric | Compression + Lineage | Probabilistic |
|--------|----------------------|--------------|
| **Commitment Stability (n=10)** | 0.94 ± 0.03 | 0.42 ± 0.12 |
| **Identity Preservation** | 92% | 38% |
| **Drift Rate (per iteration)** | 0.006 | 0.058 |

### Harness v2 results (lossy backend, drift=0.4, depth=10)

25-signal canonical corpus across 5 categories:

| | Baseline | Enforced | Gap |
|---|---|---|---|
| **Avg Final Fidelity** | 0.000 | 0.479 | **+47.9pp** |
| **Contract (payment + penalty)** | 0.000 | 1.000 | **+100.0pp** |
| **Security (encrypt + prohibit)** | 0.000 | 0.500 | **+50.0pp** |

Baseline → 0.0 (commitments destroyed, text becomes "Pretty straightforward. Nothing too complicated here.")
Enforced → holds (modal-bearing sentences survive compression).

---

## Version History

| Version | Label | Date | DOI |
|---------|-------|------|-----|
| V.1-preprint | Law Disclosure | Jan 12, 2026 | [10.5281/zenodo.18267279](https://doi.org/10.5281/zenodo.18267279) |
| V.02 | Preprint | Jan 16, 2026 | [10.5281/zenodo.18271102](https://doi.org/10.5281/zenodo.18271102) |
| V.03 | Falsifiability Testing | Jan 16, 2026 | [10.5281/zenodo.18274930](https://doi.org/10.5281/zenodo.18274930) |
| V.04 | Technical Structure Depth | Feb 26, 2026 | [10.5281/zenodo.18792459](https://doi.org/10.5281/zenodo.18792459) |

---

## Resources

- **Zenodo (DOI, all versions):** <https://doi.org/10.5281/zenodo.18267278>
- **Zenodo (current version — v0.04):** <https://doi.org/10.5281/zenodo.18792459>
- **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
**Contact:** burnmyday@proton.me

For academic or research correspondence, please reference the Zenodo DOI above.

---

## Intellectual Property Notice

**MO§ES™** (Minimal Orthogonal Subset to Essential Structure) 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.