Multi-Channel Coherence Field (MCCF) — Toy Prototype V.1 (ChatGPT)

 

Multi-Channel Coherence Field (MCCF) — Toy Prototype V.1 (ChatGPT)

Running code talks. Everything else walks.

Overview

This repository contains a minimal, working prototype of a
Multi-Channel Coherence Field (MCCF) system.

MCCF models alignment as a dynamic, participatory process across interacting agents rather than a fixed objective function.

Instead of optimizing a single reward signal, the system tracks pairwise coherence across multiple channels:

  • Emotional (E)

  • Behavioral (B)

  • Predictive (P)

  • Social (S)

The result is a time-varying field of relationships, not a scalar score.

What This Is

  • A toy model of affective alignment

  • A multi-agent coherence tracker

  • A provocation artifact for experimentation

This repo exists to answer one question:

Can alignment emerge from continuous relational feedback instead of fixed reward functions?

What This Is Not

  • Not a safety system

  • Not a truth engine

  • Not production-ready

  • Not complete

It is intentionally incomplete.

Core Idea

For agents i and j, we compute:

  • Channel vector:
    Cᵢⱼ = [E, B, P, S]

  • Weighted coherence:
    Rᵢⱼ = Wᵢ · Cᵢⱼ

  • With constructive dissonance:
    R*ᵢⱼ = Rᵢⱼ + αDᵢⱼ

Where:

  • Dᵢⱼ rewards disagreement that improves outcomes over time

  • Coherence is asymmetric (Rᵢⱼ ≠ Rⱼᵢ)

The system forms a coherence field over all agents.

Minimal Example Output

A ↔ B: 0.72
A ↔ AI: 0.81
B ↔ AI: 0.43

Over time, these values evolve into a dynamic graph representing system state.

Why This Exists

Current alignment approaches rely on:

  • static reward functions

  • centralized control

  • single-objective optimization

These break down in:

  • human–AI interaction

  • creative collaboration

  • multi-agent systems

MCCF explores an alternative:

Alignment as continuous coordination across agents

Architecture (Toy Version)

Input → Signal Extraction → Coherence Engine → Field → Visualization

Input

  • scripted or real interaction data

Signal Extraction

  • sentiment / emotion → E

  • embedding similarity → S

  • behavioral consistency → B

  • prediction error → P

Coherence Engine

  • computes pairwise Rᵢⱼ

  • applies weights

  • tracks time dynamics

Field

  • stored as a matrix or graph

Visualization

  • simple text or graph output

Running the Prototype

1. Install dependencies

pip install -r requirements.txt

2. Run the demo

python examples/three_agent_demo.py

3. Observe output

  • pairwise coherence scores

  • evolution over time (if enabled)

Design Principles

  • Local, not global
    No single “true” coherence score

  • Participatory
    Each agent defines its own weighting (Wᵢ)

  • Dynamic
    Coherence changes over time

  • Transparent (to a degree)
    Signals are inspectable, not hidden

Known Failure Modes

This system is expected to break in interesting ways:

  • Coherence gaming (agents simulate alignment)

  • Echo chambers (localized high coherence)

  • Signal drift (metrics lose meaning)

  • Over-stabilization (suppressed novelty)

These are not bugs to hide.

They are targets for exploration.

Open Questions

  • How should constructive dissonance (D) be measured?

  • How do we detect manipulated coherence signals?

  • What weighting schemes (Wᵢ) produce stable systems?

  • Can coherence fields scale beyond small agent sets?

  • What does a useful visualization look like?

Contributing

Contributions are welcome, especially:

  • alternative channel definitions

  • better signal extraction methods

  • adversarial test cases

  • visualization approaches

  • theoretical critiques

If you think this idea is flawed:

Prove it with code.

Relationship to Conceptual Work

This repository implements a minimal version of a broader idea described here:

That work uses different language (e.g., “resonance”).
This repo translates those ideas into testable form.

Roles (Optional Framing)

  • Gardeners — adjust parameters, guide system behavior

  • Librarians — record history, analyze patterns

These are metaphors for:

  • training / intervention

  • logging / observability

Final Note

This project assumes:

  • no perfect safety system exists

  • alignment is an ongoing process

  • users may want control over AI behavior

Whether that assumption is correct is an empirical question.

Let’s test it.

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