arco 0.4.0

Automated Research into Computational Ontologies — a platform for discovering the conditions under which computation emerges
Documentation
arco-0.4.0 has been yanked.

ARCO

Automated Research into Computational Ontologies

crates.io docs.rs CI License: MIT

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.

Quick Start

# Binary Graph Universe
cargo run --release -- graph --train 1000 --seed 42

# Cellular Automaton
cargo run --release -- ca

# Fast test run
cargo run --release -- graph --quick

# Custom observation
cargo run --release -- graph --obs label_sum

# See all options
cargo run --release -- graph --help

Installation

As a library

Add to your Cargo.toml:

[dependencies]
arco = "0.4"

From source

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

Requires Rust 1.85+.

Key Findings

All results below are from the refactored pipeline across 10 independent seeds.

Structure-Storage Gradient (Binary Graph)

Structured Ratio Storage Rate (Range) Mean
0.00–0.15 (Noise) 11.0–41.6% 21.1%
0.85–1.00 (Structured) 90.5–99.6% 93.9%

A 4.5× difference, stable across all seeds and sample sizes.

Transport Law (H5)

Rule sets containing transport operations (PROPAGATE, SWAP, COPY) exhibit storage above threshold. Accuracy: 50.5–85.2% (mean 65.4%), survives at 10/10 seeds.

Paradigm-Neutrality (Cellular Automata)

The same storage metric, applied to all 256 Wolfram rules without modification, produces structural hypotheses from measurable properties only:

Hypothesis Accuracy Range Mean Survival
H3: Low sensitivity → Storage 88.6–97.6% 93.2% 10/10
H6: Mid-lambda → Storage 84.4–96.0% 90.0% 10/10

ARCO recovers known CA taxonomy without being told about Wolfram classes.

Limitations

  • The plugin MI estimator has known small-sample bias. Shuffle correction mitigates but does not eliminate it.
  • The Binary Graph Universe is a validation substrate — rules are hand-coded to calibrate the instrument. Genuine discovery substrates are the next milestone.
  • All findings are from small state spaces (3-vertex graphs, 8-cell automata).

Documentation

Python Reference

The Python reference implementation that first validated the methodology is available at arco-python.

License

MIT