The Cosmic Mirror
An exploratory archive on emergent structure in discrete systems
Overview
This repository documents a personal experimental exploration into a simple but persistent question:
Can structured, law-like behavior emerge from pure information alone?
The project investigates whether discrete computational rules — specifically cellular automata — can generate large-scale structures that resemble geometry, physical correlations, and self-organizing informational patterns.
This is not a theory of everything, not a proof of fundamental physics, and not a claim about consciousness.
It is an archival experiment — a record of something that appeared interesting, reproducible, and potentially meaningful.
If it is ever useful, it will already be here...
The Cosmic Mirror
An exploratory archive on emergent structure in discrete systems
Overview
This repository documents a personal experimental exploration into a simple but persistent question:
Can structured, law-like behavior emerge from pure information alone?
The project investigates whether discrete computational rules — specifically cellular automata — can generate large-scale structures that resemble geometry, physical correlations, and self-organizing informational patterns.
This is not a theory of everything, not a proof of fundamental physics, and not a claim about consciousness.
It is an archival experiment — a record of something that appeared interesting, reproducible, and potentially meaningful.
If it is ever useful, it will already be here.
Core Idea
A simple computational rule can generate complex behavior.
From that behavior, one can construct a graph of causal and spatial relations.
From that graph, one can measure:
- local density
- curvature-like quantities
- structural motifs such as loops and triangles
The central observation explored here is modest:
Certain rules appear to generate non-random, geometrically structured graphs, whose statistical behavior differs significantly from random networks.
Nothing more is claimed — and nothing less is hidden.
Project Structure
0. Baseline — Random Reference
0_baseline_random.py
Establishes a null hypothesis using Erdős–Rényi random graphs.
Used to verify that observed correlations are not mathematical tautologies.
1. Discovery
1_discovery.py
Searches for cellular automaton rules that produce long-lived, structured dynamics (often referred to as “Class 4” behavior).
The rule 0x6a9c24b1 was selected as a reproducible example.
No claim is made that this rule is unique or optimal.
2. Physics-Inspired Analysis
2_physics.py
Builds a causal graph from the cellular automaton evolution.
Applies Forman–Ricci curvature (and augmented variants) as structural probes.
Observed result:
- Random graphs show curvature almost entirely determined by node degree.
- The generated universe graph shows significantly weaker linear dependence, suggesting additional geometric structure.
This is interpreted as structure-sensitive behavior, not literal gravity.
3. Consciousness (Interpretive Layer)
3_consciousness.py
Explores topological features (loops, persistent structures) using tools from Topological Data Analysis.
Important clarification:
This project does not measure consciousness.
Instead, topology is used as a lens to examine self-referential informational organization.
The results suggest that:
Certain informational systems naturally form stable, recurring relational patterns.
Whether such patterns relate to awareness or experience is a philosophical question, not a scientific conclusion of this repository.
4. Unification Experiment
4_unification.py
Explores whether physically stable structure and topological complexity tend to co-emerge within the same system.
This step is exploratory and narrative in nature.
It is intended to ask questions — not to answer them.
5. Sanity Checks
5_sanity_check_v2.py
Introduces stricter tests to separate structure from noise:
- augmented curvature including triangle contributions
- comparison against random graphs
- statistical testing (KS test, Welch test)
Key observation:
The cellular-automaton-derived graphs exhibit dramatically higher clustering and non-random curvature behavior compared to random networks.
This does not imply real physics — only that the structure is not trivial.
On Consciousness
This repository does not claim to explain consciousness.
Consciousness is treated here only as an interpretive perspective:
If consciousness exists in the universe, it must be compatible with structure, memory, and self-reference.
The experiments explore whether such structural prerequisites can arise naturally from simple rules.
The universe simulated here does not “become conscious”.
But it may hint at why consciousness is possible at all.
About This Project
This work was created outside academia.
No institutional backing. No funding. No expectation of recognition.
The goal was simply this:
If something interesting appears, do not let it disappear.
The repository is offered as an open archive.
Anyone is free to test it, critique it, modify it, or ignore it entirely.
Authorship & Collaboration
This project emerged through interaction between:
- A human author — curiosity, intuition, and persistence
- AI systems — ChatGPT (OpenAI), Gemini (Google), and DeepSeek
The AI systems contributed ideas, structure, and implementation suggestions.
All final choices, interpretation, and responsibility belong to the human author.
This repository exists at the intersection of human curiosity and artificial reasoning.
Final Note
This work does not attempt to explain the universe.
It merely asks whether:
Order can arise without being designed.
If someday someone finds this useful — even as a starting point — then it has already fulfilled its purpose.
Otherwise, it remains here quietly.
Like a mirror.
Author: Árva Tamás Status: archival / exploratory License: open for exploration