Datasets:

COINjecture
/

v5

	---
	license: mit
	task_categories:
	- tabular-classification
	- tabular-regression
	language:
	- en
	tags:
	- blockchain
	- np-hard
	- computational-complexity
	- proof-of-useful-work
	- cryptography
	- optimization
	- sat
	- tsp
	- subset-sum
	pretty_name: COINjecture NP Solutions v5
	size_categories:
	- 10K<n<100K
	---

	<div align="center">

	# 🧮 COINjecture NP Solutions v5

	### The Empirical Evolution

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

	[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
	[![Dataset](https://img.shields.io/badge/🤗-Dataset-blue)](https://huggingface.co/datasets/COINjecture/v5)
	[![Network](https://img.shields.io/badge/Network-Live-brightgreen)](https://coinjecture.com)

	[Documentation](https://coinjecture.com/docs) • [GitHub](https://github.com/Quigles1337/GITHUB-SUCKS) • [Website](https://coinjecture.com)

	</div>

	---

	## 📋 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

	```json
	{
	"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

	```python
	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

	```python
	# 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

	```python
	# 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](https://huggingface.co/datasets/COINjecture/NP_Solutions_v4) \|
	\| v3 Dataset \| [COINjecture/NP_Solutions_v3](https://huggingface.co/datasets/COINjecture/NP_Solutions_v3) \|
	\| GitHub \| [Quigles1337/GITHUB-SUCKS](https://github.com/Quigles1337/GITHUB-SUCKS) \|
	\| Website \| [coinjecture.com](https://coinjecture.com) \|

	---

	## 📜 Citation

	```bibtex
	@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.

	---

	<div align="center">

	Built with 🧠 by the COINjecture community

	Where every hash solves something meaningful

	</div>