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. Ecosystem dynamics with full observability — population, disease, energy, all tracked per tick
2. Emergent phenomena from simple rules — no behavior was hardcoded
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:

Field	Type	Description
tick	int	Simulation step (1-2000)
alive	int	Living entities
males	int	Male count
females	int	Female count
sick	int	Currently infected
max_gen	int	Highest generation alive
avg_energy	float	Mean energy across population
avg_body	float	Mean body size (function count)
avg_mind	float	Mean mind size (knowledge nodes)
max_body	int	Largest body
max_mind	int	Largest mind
avg_age	float	Mean age in ticks
born	int	Births this tick
died	int	Deaths this tick
eaten_pred	int	Predation kills this tick
mated	int	Successful matings this tick
infections	int	New infections this tick
famine	bool	Famine period active

Key Statistics

Metric	Value
Max generation	22
Total born	4,785
Total predation kills	3,901
Total infections	6,447
Peak population	515 (tick 327)
Min population	14 (tick 56)
Peak pandemic	69% infected (tick 556)
Mass extinctions	3 (ticks 600, 1200, 1800)
Carrying capacity	~300-400 (emerged, not hardcoded)

Emergent Phenomena Captured

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