arco 0.4.1

Automated Research into Computational Ontologies — a platform for discovering the conditions under which computation emerges
Documentation
arco-0.4.1 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 --features serialize -- graph --train 1000 --seed 42

# Cellular Automaton
cargo run --release --features serialize -- ca

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

# Save results to JSON
cargo run --release --features serialize -- graph --output results.json

# 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 from 10 independent seeds. Reproduce with:

./scripts/sweep.sh
python3 scripts/analyze.py sweep_data

Graph Substrate — n=1,000

Structure-Storage Gradient

Bracket Storage Rate Range Mean
Noise (0.00–0.15) 11.0–41.6% 21.1%
Balanced (0.40–0.60) 20.8–71.3% 35.2%
Structured (0.85–1.00) 90.5–99.6% 93.9%

A 4.5× difference. This is ARCO's most robust finding.

Hypothesis Survival

ID Condition Survival Acc. Range Mean
H2 Majority structured → memory 10/10 54.8–86.6% 68.2%
H5 Transport rules → storage 10/10 50.5–85.2% 65.4%
H7 Multiple logic gates → memory 8/10 47.2–79.5% 63.3%
H3 Logic gate → memory 7/10 38.6–80.5% 57.8%

H5 (Transport Law) and H2 are the most reliable. H1, H4, H6, H8 did not survive (0/10). H6 is a negative control (all-destructive → storage) — its consistent failure validates calibration.

CA Substrate — n=256

Null distribution sampled from a pool of known chaotic rules. All hypotheses use measurable properties only.

Hypothesis Survival

ID Condition Survival Acc. Range Mean
H3 Low sensitivity → storage 10/10 74.8–91.2% 84.9%
H6 Mid-lambda → storage 10/10 74.2–88.7% 83.5%
H5 Not Rule 0 → storage 10/10 73.3–88.0% 82.4%
H2 Parity conservation → storage 10/10 64.7–100.0% 78.4%
H1 Reversible → storage 8/10 30.0–100.0% 56.9%
H4 Even rule number → storage 0/10 — —

H4 failed because the chaotic null pool contains both even (30, 86, 106) and odd (45, 135, 149) rules with overlapping storage distributions. Evenness does not discriminate.

Limitations

  • The plugin mutual information 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. Discovery substrates are the next milestone.
  • All findings are from small state spaces (3-vertex graphs, 8-cell automata).

Documentation

Reproducibility

./scripts/sweep.sh                    # Run 10-seed sweep, save JSON
python3 scripts/analyze.py sweep_data # Analyze and generate plots

Every number in this README is traceable to a specific seed in sweep_data/ produced by scripts/sweep.sh.

Python Reference

The Python reference implementation that first validated the methodology: arco-python.

License

MIT