harness/README.md

Commitment Conservation Test Harness

This directory contains the test harness for validating the commitment conservation law under compression and recursive application.

Overview

The harness implements a falsification framework that operationalizes commitment invariance using:

• Compression-based stress tests
• Lineage-aware evaluation
• Model-agnostic testing infrastructure

Structure

• src/ - Core harness implementation

  • harness.py - Main test harness
  • test_harness.py - Test framework
  • extraction.py - Commitment extraction
  • metrics.py - Evaluation metrics
  • samples.py - Sample data management
  • plotting.py - Visualization utilities
  • config.py - Configuration management
  • deterministic_pipeline.py - Deterministic testing pipeline
  • advanced_extractor.py - Advanced extraction methods

• tests/ - Unit and integration tests

  • test_harness.py - Harness tests
  • test_full_harness.py - Full integration tests

Configuration Files

• requirements.txt - Python dependencies
• pyproject.toml - Project configuration
• environment.yml - Conda environment specification
• Harnesstest.ini - Harness configuration

Quick Start

# Install dependencies
pip install -r requirements.txt
python -m spacy download en_core_web_sm

# Extract commitments from text
python analyze.py "You must complete this by Friday."

# Run experiments (compression/recursion tests)
python analyze.py run compression --signal "You must complete this by Friday."
python analyze.py run recursion --signal "Contract terms apply." --depth 5

Installation

# Install dependencies
pip install -r requirements.txt

# Download spaCy model
python -m spacy download en_core_web_sm

CLI Usage

Quick test — Extract commitments from text:

python analyze.py "You must complete this by Friday."
python analyze.py "Contract terms apply." --quiet
python analyze.py "You shall deliver by Monday." --json

Experiments — Run compression/recursion tests and generate receipts:

python analyze.py run compression --signal "You must complete this by Friday."
python analyze.py run recursion --signal "You must pay $100." --depth 5
python analyze.py run full

Experimental runs output timestamped JSON receipts to outputs/ directory.

Running Tests

# Run all tests
MPLBACKEND=Agg pytest tests/test_full_harness.py -v

# Quick test run
MPLBACKEND=Agg pytest tests/test_full_harness.py -q

CLI Commands

compression

Tests commitment conservation under compression transformations.

Options:

• --signal TEXT - Input signal text (required)
• --out PATH - Output receipt path (default: outputs/compression_receipt.json)

recursion

Tests commitment drift under recursive transformations.

Options:

• --signal TEXT - Input signal text (required)
• --depth N - Recursion depth (default: 8)
• --enforced - Enable enforcement mode
• --out PATH - Output receipt path (default: outputs/recursion_receipt.json)

full

Runs the complete deterministic pipeline.

Options:

• --out PATH - Output receipt path (default: outputs/full_receipt.json)

Output

The CLI generates:

• JSON receipts in outputs/ with all experimental data
• Compression fidelity plots (fid_*.png)
• Recursion drift plots (delta_*.png)

Purpose

This harness is designed to test whether commitment content is conserved under:

1. Compression - Reduction to essential structure
2. Recursion - Repeated self-application with lineage tracking

The framework is model-agnostic and can be applied to both human and machine-generated language.

Environment Notes

This harness specifies the structure and invariants of the evaluation. Exact dependency resolution may vary across systems (OS, Python, backend). Environment-related failures should be distinguished from invariant violations.

Citation

If you use this harness, please cite the original paper:

McHenry, D. J. (2026). A Conservation Law for Commitment in Language Under 
Transformative Compression and Recursive Application. Zenodo. 
DOI: 10.5281/zenodo.18267279