jkdkr2439/Primordial-Evolution

Primordial: Artificial Life Evolution Dataset

Tick-by-tick evolution data from digital organisms with body (executable code) + mind (structured knowledge). Sexual reproduction, pandemics, mass extinction, predation — all emerged from simple rules.

Why This Dataset Exists

Most AI datasets capture static snapshots. This dataset captures dynamic evolution — digital organisms eating code, reproducing sexually, getting sick, dying, and being selected by nature over 2000 ticks.

No existing dataset provides:

1. 1.Ecosystem dynamics with full observability — population, disease, energy, all tracked per tick
2. 2.Emergent phenomena from simple rules — no behavior was hardcoded
3. 3.Sexual reproduction + pandemic cycles + mass extinction in one simulation

Dataset Contents

train.parquet — Evolution Log (2000 records, Parquet format)

Also available as sim3_evolution.jsonl (raw JSONL).

One record per simulation tick. 60 initial entities, 2000 ticks.

18 fields per record:

Key Statistics

Emergent Phenomena Captured

1. 1.Ecological oscillation — population self-regulates around carrying capacity without any target
2. 2.Pandemic waves — disease peaks then declines as immune memory spreads; new strains restart cycle
3. 3.Post-extinction recovery — after 50% die-off, population rebounds within ~100 ticks; survivors are fitter
4. 4.Gender homeostasis — M/F ratio stays ~50:50 despite stochastic assignment
5. 5.Generation acceleration then stabilization — early gens appear fast, then stabilize to ~90-100 ticks/gen

How To Use

Load with HuggingFace

from datasets import load_dataset
ds = load_dataset("jkdkr2439/Primordial-Evolution")

Load directly

import pandas as pd
df = pd.read_parquet("train.parquet")
df.plot(x="tick", y=["alive", "sick"], figsize=(12, 4))

Prediction task

# Predict next 100 ticks from previous 100
# Input: df[0:100], Output: df[100:200]
# Features: alive, sick, avg_energy, born, died, famine

Connection to Consciousness Research

This dataset comes from the Primordial project — a computational framework studying emergence of self-reflective behavior in digital organisms, with implications for the Hard Problem of Consciousness (Chalmers, 1995).

Entity Architecture

Entity = Body (Python functions) + Mind (NMF knowledge graph)
  - Body: digest code, build new functions, decay unused ones
  - Mind: absorb knowledge, compress to DNA, decay unused nodes
  - Sex: M encodes gamete (cheap), F decodes + builds child (expensive)
  - Immune: antibody memory, virus corrupts body functions
  - Lifecycle: Vo (dormant) > Sinh (born) > Dan (growing) > Chuyen (transform)

Physics Laws (all hardcoded, no entity can break)

• —Syntax validity: ast.parse() — invalid code = dead
• —Energy conservation: eating gives energy, existing costs energy
• —Complexity cost: more code = more expensive to maintain
• —Aging: older entities cost exponentially more
• —Famine: every 250 ticks, food drops to 30%
• —Extinction: every 600 ticks, bottom 50% killed

Author

Tung Nguyen (Kevin T.N.)

License

MIT