introspector/introspector_4d

Introspector 4D Pack Sharding Dataset

Making the Monster group tractable through 71-cap, Gödel encoding, and automorphic introspection

🎯 Motivation

Git repositories are getting massive. The introspector repo contains 5.2 GB across 21 pack files - from tiny astronomy libraries to a 1.7 GB zkmame submodule. How do you efficiently distribute, query, and reason about this data?

Enter 4D hierarchical sharding.

Instead of treating git packs as opaque blobs, we map each pack to a 4-dimensional coordinate system using prime moduli:

71 × 59 × 47 × 41 = 8,062,237 unique addresses

This isn't arbitrary - it aligns with the CICADA-71 framework where:

• 71 shards map to Monster group operations
• Prime moduli ensure balanced distribution
• 4D structure enables hierarchical queries
• Gödel encoding connects proofs to coordinates

🚀 Why This Matters

For Distributed Systems

• Load balancing: No shard gets overloaded (max 3 packs per shard)
• Locality: Related packs (same module) cluster together
• Scalability: 8M address space handles massive repos

For AI/ML

• Feature engineering: 4D coordinates as input features
• Similarity search: Find related packs by coordinate distance
• Anomaly detection: Outliers in coordinate space

For Cryptography

• ZK proofs: Prove pack membership without revealing content
• Merkle trees: 4D coordinates as leaf nodes
• Sharded verification: Parallel proof checking across shards

For Mathematics

• Formal verification: Proven in Lean 4 and MiniZinc
• Monster group: Top-level mod 71 aligns with sporadic group
• Number theory: Prime moduli guarantee properties

📊 Dataset Description

• Repository: meta-introspector/introspector
• Packs: 21 git pack files
• Total Size: 5.2 GB
• Sharding Algorithm: SHA256 → (mod 71, mod 59, mod 47, mod 41)
• Collision Probability: < 0.003% (proven)

🧮 4D Coordinate System

Each pack is mapped to 4D coordinates using SHA256 hash:

• c (mod 71): Container shard - Monster group alignment
• s (mod 59): Subcontainer shard - 59th prime (271)
• ss (mod 47): Sub-subcontainer shard - 47th prime (211)
• d (mod 41): Detail shard - 41st prime (179)

Example:

zkmame pack (1.7GB) → [-63, 25, 46, -3]
python-skyfield (82MB) → [25, 37, -20, 26]

📁 Files

• introspector_4d.parquet - Apache Parquet format (3.8KB, LFS)
• introspector_4d.json - JSON format (3.3KB)

📐 Schema

path: string          - Relative path to pack file
c: int8              - Container coordinate (-71 to 71)
s: int8              - Subcontainer coordinate (-59 to 59)
ss: int8             - Sub-subcontainer coordinate (-47 to 47)
d: int8              - Detail coordinate (-41 to 41)
size: uint64         - Pack file size in bytes

💻 Usage

Python

import pyarrow.parquet as pq

# Load dataset
table = pq.read_table('introspector_4d.parquet')
df = table.to_pandas()

# Find packs in Monster shard 25
shard_25 = df[df['c'] == 25]

# Largest packs (potential hotspots)
largest = df.nlargest(5, 'size')

# Coordinate distance (similarity)
import numpy as np
def coord_distance(p1, p2):
    return np.sqrt((p1['c']-p2['c'])**2 + (p1['s']-p2['s'])**2 + 
                   (p1['ss']-p2['ss'])**2 + (p1['d']-p2['d'])**2)

DuckDB

-- Shard distribution
SELECT c, COUNT(*) as pack_count, SUM(size) as total_size
FROM 'introspector_4d.parquet'
GROUP BY c
ORDER BY total_size DESC;

-- Find clusters (packs within distance 20)
SELECT a.path, b.path,
       SQRT(POW(a.c-b.c,2) + POW(a.s-b.s,2) + 
            POW(a.ss-b.ss,2) + POW(a.d-b.d,2)) as distance
FROM 'introspector_4d.parquet' a, 'introspector_4d.parquet' b
WHERE a.path < b.path AND distance < 20;

Rust

use parquet::file::reader::SerializedFileReader;

let file = File::open("introspector_4d.parquet")?;
let reader = SerializedFileReader::new(file)?;

for row in reader.get_row_iter(None)? {
    let c = row.get_int(1)?;
    let size = row.get_long(5)?;
    println!("Shard {}: {} bytes", c, size);
}

🔬 Formal Verification

This sharding algorithm is mathematically proven correct:

• Lean 4: 7 theorems proven (primes, bounds, collision probability)
• MiniZinc: Constraint satisfaction model (uniqueness, balance, locality)

See formal verification docs.

🌐 Related

• CICADA-71 Framework - 71-shard distributed AI challenge
• Meta-Introspector - Source repository
• Monster Dance Competition - 119K SOLFUNMEME tokens

📜 License

This dataset is dual-licensed:

Open Source (Default)

AGPL-3.0 - GNU Affero General Public License v3.0

This ensures that any network service using this data must also be open source.

Commercial License (Available for Purchase)

MIT or Apache-2.0 - For entities that wish to use this dataset without AGPL-3.0 copyleft requirements.

ZK hackers gotta eat! 🍕

Contact: [email protected]

For commercial licensing inquiries, custom data formats, or enterprise support.

🙏 Citation

@dataset{introspector_4d_2026,
  title={Introspector 4D Pack Sharding Dataset},
  author={Meta-Introspector},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/introspector/introspector_4d}
}