How to Enforce Commitment Conservation — MO§ES

Enforcing commitment conservation is the core operational task of the MO§ES framework. The Conservation Law of Commitment predicts that without enforcement, recursive transformations degrade semantic commitment by 15-20% per iteration. This guide walks you through implementing the four enforcement mechanisms that make commitment conservation operational: pre-execution gating, lineage binding, resonance thresholding, and audit trail setup.

Overview

Commitment conservation enforcement means ensuring that C(T(S)) ≈ C(S) — the commitment of a transformed signal approximately equals the commitment of the original — across every transformation in your AI pipeline. This requires four mechanisms working together: a gate that checks commitment before transformation, a lineage binding that ties outputs to origins, a resonance threshold that rejects degrading transformations, and an audit trail that records every decision.

By the end of this guide, you will have a working enforcement layer that can be applied to any transformation pipeline — summarization, translation, agent orchestration, or multi-agent communication.

Prerequisites

• A working AI transformation pipeline (summarization, translation, agent chain, etc.)
• An NLI model for bidirectional entailment measurement (e.g., a DeBERTa-based entailment classifier)
• A SHA-256 hashing library (available in every standard programming environment)
• A 20-signal canonical corpus with known commitment levels for testing (see the papers page for the reference corpus)
• Familiarity with the Conservation Law of Commitment and the MO§ES architecture

Step 1: Define Your Commitment Threshold

The commitment threshold is the minimum acceptable commitment level for transformed signals in your pipeline. This is the single most important configuration decision. Set it too high and you will reject legitimate transformations; set it too low and you allow commitment degradation.

A threshold of 0.80 is a reasonable starting point — it means a transformed signal must retain at least 80% of the original commitment. In the MO§ES experimental record, enforced pipelines conserved commitment at 80-85% across 10 iterations. Without enforcement, commitment fell below 20% by iteration 5.

COMMITMENT_THRESHOLD = 0.80  # minimum acceptable C(T(S)) / C(S)

Document your threshold choice and the rationale. Different domains may require different thresholds — a legal contract pipeline may demand 0.95, while a creative summarization pipeline may accept 0.70.

Step 2: Implement Pre-Execution Gating

Pre-execution gating is the first line of defense. Before any transformation is applied, the gate measures the commitment of the input signal and predicts whether the transformation will preserve commitment above the threshold.

The gate does not need to be perfect — it needs to be conservative. If in doubt, block and escalate. The cost of a false negative (allowing a degrading transformation) is semantic drift that compounds with recursion depth. The cost of a false positive (blocking a safe transformation) is a retry or human review.

def gate(signal, transform, threshold):
    c_before = measure_commitment(signal)
    predicted_output = transform(signal)
    c_after = measure_commitment(predicted_output)
    ratio = c_after / c_before
    if ratio < threshold:
        log_rejection(signal, predicted_output, ratio)
        return None  # block
    return predicted_output  # allow

Step 3: Bind Lineage to Every Signal

Lineage binding cryptographically ties every transformed signal to its origin. This is the mechanism described in the Lineage Claw concept. Every signal carries a SHA-256 hash of its origin, the transformation that produced it, and a timestamp.

import hashlib, json

def bind_lineage(origin_signal, transform_type, output_signal):
    origin_hash = hashlib.sha256(origin_signal.encode()).hexdigest()
    output_hash = hashlib.sha256(output_signal.encode()).hexdigest()
    return {
        "origin_hash": origin_hash,
        "output_hash": output_hash,
        "transform": transform_type,
        "timestamp": datetime.utcnow().isoformat()
    }

This binding creates a verifiable chain of custody. At any point, you can trace a signal back to its origin and verify that the transformation chain is intact. This is essential for auditability and for detecting where degradation occurred.

Step 4: Apply Resonance Thresholding

After the transformation is applied, measure the commitment of the output signal using two complementary methods:

• NLI bidirectional entailment: Measures semantic preservation by checking whether the original and transformed signals entail each other. High mutual entailment means the semantic content is preserved.
• Jaccard surface stability: Measures lexical preservation by computing the Jaccard similarity between the token sets of the original and transformed signals. This catches surface-level drift even when semantics are preserved.

If either measurement falls below the threshold, reject the transformation. Log the rejection with the measured values so you can tune the threshold over time.

Step 5: Configure the Audit Trail

The audit trail is an append-only log that records every transformation event. Each entry includes: input hash, output hash, transformation type, commitment before and after, the enforcement decision (pass, reject, retry), and a timestamp.

The audit trail serves three purposes: it provides evidence for compliance and governance reviews, it enables post-hoc analysis of where degradation occurred, and it creates a feedback loop for threshold tuning. Without an audit trail, enforcement is invisible — you cannot prove that commitment was conserved.

Step 6: Test with a Canonical Corpus

Before deploying your enforcement pipeline to production, test it against a 20-signal canonical corpus with known commitment levels. Run 10 recursive iterations per signal and verify that commitment is conserved at or above your threshold across all iterations.

The MO§ES experimental record provides a reference corpus and expected results. If your pipeline conserves commitment at 80-85% across 10 iterations, your enforcement is working. If commitment degrades, identify which step is failing — the gate, the lineage binding, or the threshold — and adjust accordingly.

Common Pitfalls

• Setting the threshold too low: A threshold below 0.70 allows significant semantic drift. Start at 0.80 and adjust based on your domain.
• Skipping pre-execution gating: Without the gate, degrading transformations are applied before they can be caught. The gate is your first line of defense.
• Not logging rejections: Rejections are data. Without logging them, you cannot tune your threshold or identify systematic degradation patterns.
• Using only one measurement method: NLI alone misses surface drift; Jaccard alone misses semantic drift. Use both.
• Forgetting to test recursively: Single-transform tests will pass even without enforcement. The degradation only appears under recursive transformation — always test with 10+ iterations.

Next Steps

Once your enforcement pipeline is operational, consider extending it with multi-agent audit capabilities and lineage verification. For a deeper understanding of the theory, read the Conservation Law of Commitment and the full research papers.