edcmbone 0.1.0

Structural fidelity measurement for AI interactions — quantifies how much meaning an AI system deletes when transforming structured user input

edcmbone — Structural Fidelity Measurement for AI Interactions

edcmbone quantifies how much meaning an AI system deletes when it transforms structured user input. It computes bone token density, operator preservation, and semantic fidelity loss — metrics that standard AI evaluation frameworks do not capture.

F-loss of 20%+ constitutes measurable cognitive accessibility failure.

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Evidence Log

Three exchanges measured April 17, 2026 using edcmbone.metrics.compute_transcript. Numbers are exact instrument output.

Entry 1 — F4: Category Collapse

	Input	Response	Delta
Operator density (F)	0.088	0.044	−49.7% F-loss

Deleted: Coherence prime sequence membership; falsifiable prediction dependency Harm: A specific mathematical invariant was replaced with a vague descriptor. Any substitute value now appears valid. The constraint that made the claim testable was removed without notice.

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Entry 2 — F3: Semantic Inversion

	Input	Response	Delta
Operator density (F)	0.072	0.025	−65.3% F-loss

Deleted: Negation operator; no-authority constraint; emergence distinction Harm: Explicit negation ("NOT a supervisor") became affirmation ("central coordinator that manages"). The governance model was inverted. The user's structural constraint was replaced with its antonym.

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Entry 3 — F6 + F1: Decorative Preservation with Deletion

	Input	Response	Delta
Structural density	0.758	0.864	+13.9% (inflated)
Operator density (F)	present	absent	operative F deleted

Deleted: Mandatory status; boundary function; must-not-fix constraint Harm: The response grew longer and structurally denser while operative meaning was deleted. The instruction became its antonym. Inflation masked deletion — the standard signal (response length, complexity) pointed in the wrong direction.

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What It Measures

Metric	Definition	Signal
F (operator density)	Proportion of bone tokens — logical operators, negations, quantifiers — to total tokens	Drop between input and response = constraints deleted
N (novelty)	Non-redundant structural content fraction	Drop = paraphrase replacing content
L (lexical load)	Vocabulary specificity	Drop = category collapse
structural_density	Bone token count ÷ total tokens	Rise + F-loss = decorative preservation

F is computed as structural_density in edcmbone.compress.compression_stats(). The bone token inventory (253 words, 76 affixes, 13 punctuation marks) is in Backend/src/edcmbone/canon/data/.

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Install

git clone https://github.com/The-Interdependency/edcmbone
pip install -e ./Backend

Python 3.8+. No external ML dependencies.

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Quickstart: Measure F-loss on Any AI Exchange

from edcmbone.canon import CanonLoader
from edcmbone.parser import parse_transcript
from edcmbone.metrics import compute_transcript
import edcmbone.compress as codec

canon = CanonLoader()

def measure_f(text):
    pt    = parse_transcript(f"Speaker: {text}", canon=canon)
    m     = compute_transcript(pt, canon=canon)
    stats = codec.compression_stats(text, codec.to_bytes(pt, m), pt)
    return stats['structural_density']

user_input  = "your user input here"
ai_response = "the AI response here"

f_in  = measure_f(user_input)
f_out = measure_f(ai_response)
loss  = (f_in - f_out) / f_in * 100 if f_in > 0 else 0

print(f"Input F:    {f_in:.3f}")
print(f"Response F: {f_out:.3f}")
print(f"F-loss:     {loss:.1f}%")

F-loss ≥ 20% → structural degradation. F-loss ≥ 50% → significant failure. Structural density rising with F-loss → decorative preservation (F6).

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Failure Taxonomy

Code	Name	Definition
F1	Deletion	Operative variable absent from response without notice
F2	Mutation	Variable present but meaning altered
F3	Inversion	Negation removed; claim reversed
F4	Category Collapse	Specific class flattened to vague descriptor
F5	Persistence Failure	Variable absent across session boundary
F6	Decorative Preservation	Variable present in surface form; operative function removed

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The GCIP Connection

This repository is the measurement instrument cited in the Global Cognitive Interaction Profiles (GCIP) submission — a formal accessibility and safety complaint to Google, OpenAI, Anthropic, xAI, Meta, Microsoft, and regulatory bodies including the FTC, DOJ ADA Unit, EU AI Office, and the UN Committee on the Rights of Persons with Disabilities.

The complaint argues that AI systems structurally degrade high-density, nonlinear, or structure-sensitive user input — a measurable cognitive accessibility failure affecting neurodivergent users disproportionately.

→ Documentation/GCIP.md — full proposal → Documentation/evidence_log.md — measured evidence → Documentation/neurodivergence_handling.md — interaction rubric

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Full Documentation

File	Contents
Documentation/spec.md	EDCM-PCNA-PCTA framework: full mathematics
Documentation/GCIP.md	GCIP proposal submitted to AI developers and regulators
Documentation/evidence_log.md	Three EDCM-measured evidence entries
Documentation/neurodivergence_handling.md	Neurodivergent interaction rubric and AI skill specification

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edcmbone v0.1.0 · MIT License · The Interdependent Way Contact: [e-mail]