🧮 COINjecture NP Solutions v5

The Empirical Evolution

Live solutions to NP-hard computational problems from the COINjecture blockchain

Documentation • GitHub • Website

---

📋 Overview

This dataset contains real-time solutions to NP-hard problems mined from the COINjecture Network B blockchain. Unlike traditional Proof-of-Work that wastes computational energy, COINjecture implements Proof-of-Useful-Work (PoUW) — every hash contributes to solving computationally significant problems.

What Makes v5 Special

Feature	v4 (Previous)	v5 (Current)
Tokenomics	Hardcoded constants	100% empirical (network-derived)
Light Clients	Basic SPV	FlyClient + MMR proofs
Node Classification	Single type	6 specialized types
Mobile Support	None	WASM + C-FFI SDK
Metrics	Static	Live network oracle

---

🎯 Problem Types

Subset Sum

Given a set of integers, find a subset that sums to a target value.

• Complexity: NP-complete
• Applications: Cryptography, resource allocation, financial modeling

Boolean Satisfiability (SAT)

Determine if a Boolean formula can be satisfied.

• Complexity: NP-complete (Cook-Levin theorem)
• Applications: Hardware verification, AI planning, scheduling

Traveling Salesman Problem (TSP)

Find the shortest route visiting all cities exactly once.

• Complexity: NP-hard
• Applications: Logistics, circuit design, DNA sequencing

Custom Problems

User-submitted computational challenges with bounties.

• Complexity: Variable (verified NP-hard)
• Applications: Research, optimization, real-world problems

---

📊 Dataset Schema

{
  "problem_id": "uuid-v4",
  "problem_type": "SubsetSum | SAT3 | TSP | Custom",
  "problem_data": {
    "elements": [1, 2, 3, ...],
    "target": 42
  },
  "solution_data": {
    "selected_indices": [0, 2, 5],
    "selected_elements": [1, 3, 10]
  },
  
  "block_height": 12345,
  "timestamp": 1733500000,
  "block_hash": "0x1a2b3c...",
  "prev_block_hash": "0x9f8e7d...",
  
  "work_score": 100.0,
  "solution_quality": 1.0,
  "problem_complexity": 3.5,
  "bounty": "1000000",
  
  "solve_time_us": 150000,
  "verify_time_us": 1200,
  "energy_ratio": 1920.5,
  
  "solver": "12D3KooW...",
  "submitter": "12D3KooW...",
  
  "network_metrics": {
    "hash_rate": 1.5,
    "peer_count": 25,
    "consensus_agreement": 0.95
  }
}

Field Descriptions

Field	Type	Description
problem_id	string	Unique identifier for the problem
problem_type	enum	Category of NP-hard problem
problem_data	object	Problem-specific input data
solution_data	object	Verified solution
block_height	integer	Block number in the chain
timestamp	integer	Unix timestamp of block creation
work_score	float	Computational work performed
solution_quality	float	Optimality measure (1.0 = optimal)
problem_complexity	float	Estimated problem difficulty
bounty	string	Reward in microCOIN (u128 as string)
solve_time_us	integer	Solution time in microseconds
verify_time_us	integer	Verification time in microseconds
energy_ratio	float	Verification/solve energy ratio

---

🚀 Quick Start

Load with Hugging Face Datasets

from datasets import load_dataset

# Load the full dataset
dataset = load_dataset("COINjecture/v5")

# Iterate through solutions
for record in dataset["train"]:
    print(f"Block {record['block_height']}: {record['problem_type']}")
    print(f"  Work Score: {record['work_score']}")
    print(f"  Energy Ratio: {record.get('energy_ratio', 'N/A')}x")

Filter by Problem Type

# Get only SAT problems
sat_problems = dataset["train"].filter(
    lambda x: x["problem_type"] == "SAT3"
)

# Get only high-complexity problems
hard_problems = dataset["train"].filter(
    lambda x: x["problem_complexity"] > 4.0
)

Stream Large Datasets

# Stream without downloading entire dataset
dataset = load_dataset("COINjecture/v5", streaming=True)

for record in dataset["train"]:
    process(record)

---

📈 Statistics

Metric	Value
Update Frequency	Real-time (every block)
Avg Block Time	~30 seconds
Problem Types	4
Verification Rate	100%
Energy Asymmetry	>1000x (v5 with ADZDB)

---

🔬 Research Applications

Machine Learning

• Train models to predict problem difficulty
• Learn heuristics for NP-hard optimization
• Benchmark solver algorithms

Cryptography

• Study hash function distributions
• Analyze computational hardness assumptions
• Research post-quantum implications

Distributed Systems

• Study consensus mechanisms
• Analyze network behavior under load
• Research incentive-compatible protocols

---

🧮 Empirical Tokenomics (v5)

v5 introduces zero hardcoded constants. All economic parameters are derived from live network state:

┌─────────────────────────────────────────────────────────────────┐
│                    NETWORK METRICS ORACLE                       │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  Hash Rate    ──→  Emission Bounds                             │
│  Solve Times  ──→  Problem Hardness Factor                     │
│  Median Fees  ──→  Base Storage Cost                           │
│  Stake Dist   ──→  Staking Thresholds                          │
│  Fault Impact ──→  Reputation Severities                       │
│                                                                 │
│  Formula: value = f(network_state)                             │
│  Result:  Self-regulating, governance-free economics           │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

---

📱 LightSync Protocol

v5 implements FlyClient with Merkle Mountain Ranges for O(log n) chain verification:

Protocol	Bandwidth	Use Case
Full Sync	O(n)	Archive nodes
SPV	O(n) headers	Desktop wallets
FlyClient	O(log n)	Mobile devices

For 1M blocks:

• SPV: ~80 MB of headers
• FlyClient: ~50 KB (proofs + sampled headers)

---

🔗 Related Resources

Resource	Link
v4 Dataset	COINjecture/NP_Solutions_v4
v3 Dataset	COINjecture/NP_Solutions_v3
GitHub	Quigles1337/GITHUB-SUCKS
Website	coinjecture.com

---

📜 Citation

@dataset{coinjecture_v5_2025,
  title     = {COINjecture NP Solutions Dataset v5},
  author    = {COINjecture Network Contributors},
  year      = {2024},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/COINjecture/v5},
  note      = {Real-time NP-hard problem solutions from Proof-of-Useful-Work blockchain}
}

---

⚖️ License

This dataset is released under the MIT License. You are free to use, modify, and distribute the data for any purpose, including commercial applications.

---

Built with 🧠 by the COINjecture community

Where every hash solves something meaningful