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.

Status

v0.1.0 — Validated against the reference implementation. Core library complete.

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.

Discovered Laws

Running the scientific cycle with default parameters reproduces:

• Transport Law (H5): Rule sets containing information transport operations (PROPAGATE, SWAP, COPY_TO_OUT, COPY_FROM_IN) exhibit storage at significantly higher rates (91.2% accuracy in Python reference, 60% in Rust v0.1.0).
• Structure-Storage Gradient: Storage probability increases monotonically with the fraction of structured rules, from ~14% (pure noise) to ~91% (pure structure).
• Boolean Rediscovery: NAND, AND, OR, NOR, and XOR gates are rediscovered without explicit encoding.

Quick Start

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

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

Build

make all        # fmt, clippy, test
cargo run       # run the scientific cycle

Package Structure

Python Reference

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

License

MIT