# Reproducibility Receipt

**Paper:** A Conservation Law for Commitment in Language Under Transformative Compression and Recursive Application
**Version:** V.04 — Technical Structure Depth
**DOI:** [10.5281/zenodo.18792459](https://doi.org/10.5281/zenodo.18792459)
**Concept DOI (all versions):** [10.5281/zenodo.18267278](https://doi.org/10.5281/zenodo.18267278)

**Date**: March 6, 2026
**Status**: Harness v2.0 Confirmed Operational

## Test Execution

All 53 tests passing in test suite:

```bash
python -m pytest tests/test_harness.py -v
```

**Result**: `53 passed in 0.07s`

### Test Coverage

**Extraction (modal-pattern sieve):**
- Sentence segmentation (single, multiple, semicolons, empty)
- Classification (obligations, prohibitions, constraints)
- False positive rejection ("will", "have", soft modals)
- "must not" correctly classified as prohibition (v1 regression)
- Conditional detection
- Backward compatibility interface

**Fidelity (min-aggregated scoring):**
- Jaccard (perfect, zero, partial overlap, empty sets)
- Cosine TF-IDF (identical, paraphrased, unrelated)
- NLI proxy (modal preserved vs. destroyed)
- Min-aggregation binding

**Compression:**
- Extractive backend (compression, modal priority, passthrough)

**Enforcement:**
- Commitment gate (pass when preserved)
- Baseline (no gate)

**Lineage:**
- Hash determinism
- Commitment set hash (order-independent)
- Chain integrity validation
- Chain break detection
- JSON serialization

**Corpus:**
- 25 signals load correctly
- 5 categories present (contractual, technical, regulatory, procedural, composite)
- All signals contain extractable commitments

**Integration:**
- Single signal full protocol
- Enforcement >= baseline validation

**Regressions (v1 bugs):**
- "will" false positive blocked
- "have" false positive blocked
- Soft modals rejected
- "must not" is prohibition
- Fidelity uses multiple metrics

## Environment

- **Python**: 3.11+
- **Dependencies**: gradio, matplotlib (demo only); core harness is stdlib-only
- **Lossy backend**: Pure Python, zero external dependencies, deterministic
- **Matplotlib backend**: Agg (non-GUI, CI-friendly)

## Running Tests

### Quick run:

```bash
python -m pytest tests/test_harness.py -q
```

### Verbose output:

```bash
python -m pytest tests/test_harness.py -v
```

### Full falsification protocol (CLI):

```bash
python -m src.runner --backend lossy --depth 10
```

### Interactive demo:

```bash
python app.py
# Opens at http://localhost:7860
```

## Notes

- Tests complete in <1 second (no model loading required)
- Lossy backend is deterministic: same input -> same output -> same lineage chain
- Harness requires only `gradio` and `matplotlib` for the demo; core pipeline is stdlib-only
- Previous harness (v1) archived at `archive/harness-v1/`

---

**Harness v2.0 is research-ready for experimental evaluation and adversarial replication.**