mgos-pluralism-demo

MG-OS Demo

Related resources

Repository:https://github.com/GhostDriftTheory/mgos-pluralism-demo

Paper (preprint):https://zenodo.org/records/17712891

Project page:https://www.ghostdriftresearch.com/

Minimal theory-side demonstration of **Meaning-Generation OS (MG-OS).**

This repository visualizes two core ideas from the MG-OS preprint:

1. Minority-mode preservation in long-tail classification
2. Barrier-driven escape suppression in a double-well Langevin system

The goal is not benchmark performance. The goal is to make the theory visible in a small, self-contained form.

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What this demo shows

Exp-A: Long-tail classification

A 3-class long-tail toy problem compares:

• a baseline softmax classifier
• a barrier-style classifier inspired by the MG-OS formulation

The app displays:

• Accuracy
• Recmin (minority recall)
• OCW Trigger
• eta* (probability floor from the minority minimum margin)
• Demo Gamma = B* - C0 f_hat (paper-aligned display)

This is the visual side of minority-mode preservation.

Exp-B: Double-well Langevin dynamics

A 1D double-well system visualizes how increasing the barrier scale Gamma suppresses escape events. In the paper notation, the certified margin is:

Gamma := B* - C0 f_hat(pdel, rho, delta) > 0

In this demo, B* is tied to the visible barrier scale and f_hat is represented by observable toy-side proxy terms.

The app displays:

• the double-well potential
• sample trajectories
• kesc (escape-rate proxy)
• OCW Trigger
• a Gamma sweep table

This is the visual side of barrier-certified stability.

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Paper-aligned certificate view

For visual alignment with the MG-OS preprint, the app exposes a demo-side quantity in the same notation as the theoretical certificate:

Gamma_demo = B* - C0 * f_hat(pdel, rho, delta)

This is a theory-aligned visualization layer. It is not the full outward-rounded rational / Sigma1 certificate from the paper.

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Repository structure

• app.py — Streamlit app with both demos
• requirements.txt — minimal dependencies

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Run locally

pip install -r requirements.txt
streamlit run app.py

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Interpretation

This repository is a minimal theory demo, not a production implementation.

It is designed to make the following points visible:

• minority modes can be made harder to suppress
• a barrier margin can be treated as an operational safety quantity
• theoretical quantities can be linked to observable toy metrics
• the paper notation for Gamma can be read against the toy observables shown in the app

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Ethical note

MG-OS is not a device for declaring that non-selected meanings are worthless.

Its role is to reduce premature erasure of candidate meanings before selection. In this sense, the aim is not exclusion, but protection against collapse under majority pressure or unstable dynamics.

The ethical risk arises when the admissibility condition itself is fixed too narrowly and begins to function as an exclusion rule. For that reason, MG-OS should be understood as a structure for preserving candidate meanings prior to selection, not as a mechanism for asserting that non-selected meanings have no value.

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Scope

This demo is intentionally small.

It does not include:

• a full MG-OS implementation
• a full attention block or MoE stack
• large-scale training
• real-world deployment claims
• the full outward-rounded certificate package from the paper

It is a compact visualization layer for the theory.