ARCO

Automated Research into Computational Ontologies

A computational science platform for discovering the conditions under which computation, memory, and learning emerge in arbitrary information systems.

What ARCO Does

ARCO asks a different question than most computer science: not "what can a given computational model compute?" but "what computational models are possible, and why do they emerge?"

It formalizes this through Information Universes — 6-tuples of (state space, transformations, observations, resources, invariants, schedule) — and measures emergent computation via shuffle-corrected normalized mutual information calibrated against destructive null distributions.

Installation

As a library

Add to your Cargo.toml:

[dependencies]
arco = "0.2"

From source

git clone https://github.com/kvernet/arco.git
cd arco
cargo build --release

Quick Start

use arco::cycle::{CycleConfig, run_cycle};

fn main() {
    let config = CycleConfig::default();
    let record = run_cycle(&config);
    println!("{}", record.summary());
}

Or via the CLI:

cargo run --release
cargo run --release -- --train 1000 --test 300 --seed 42
cargo run --release -- --quick

See examples/ for more usage patterns.

Discovered Laws

Running the scientific cycle reproduces five structural laws:

• Transport Law (H5): Rule sets containing information transport operations (PROPAGATE, SWAP, COPY_TO_OUT, COPY_FROM_IN) exhibit storage at significantly higher rates (~53% accuracy at n=50,000, 91% in Python reference).
• Structure-Storage Gradient: Storage probability increases monotonically with the fraction of structured rules, from ~18% (pure noise) to ~95% (pure structure).
• Majority Structure Law (H2): Majority-structured rule sets exhibit memory (~62% accuracy).
• Logic Gate Law (H3): Rule sets containing logic gates exhibit memory (~53% accuracy).
• Multiple Logic Law (H7): Rule sets with multiple logic gates exhibit memory (~58% accuracy).

Additionally, NAND, AND, OR, NOR, and XOR gates are rediscovered without explicit encoding.

Performance

Benchmarked on a 20-core machine (n=50,000 universes, release build):

Package Structure

Building from Source

git clone https://github.com/kvernet/arco.git
cd arco
make all        # format, clippy, tests
cargo build --release
./target/release/arco --help

Documentation

• Mathematical Constitution — the formal specification for ARCO
• API documentation — rustdoc for the latest release

Python Reference

The Python reference implementation that validated the methodology is available at ARCO Python.

License

MIT