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BlockDAG Consensus & Topological Ordering Framework

RCR Labs (Ratan Core Research Labs) | Lead Architect: Amardeep Singh | Sentinel: Aarya


1. Executive Summary & Research Scope

High-velocity Proof-of-Work (PoW) consensus mechanisms operating at sub-second block times (10bps to 100bps) require rigorous mathematical modeling to bound ledger growth, order competing blocks, and maintain dynamic security parameters.

This repository provides a mathematical verification framework modeling topological partial-order DAG state transitions, evaluating Kaspa ($KAS) GHOSTDAG/PHANTOM mechanics, and tracking hash rate efficiency under asynchronous block propagation.


2. Mathematical Components

  • GHOSTDAG K-Cluster Ordering (models/ghostdag_ordering.py): Algorithmic implementation evaluating blue/red block selection rules and topological sequence sorting.
  • Consensus Boundary Simulator: Analytical tools for evaluating DAG depth, anticone sizing, and thermal/mining node performance parameters.

3. Directory Structure

blockdag-consensus-framework/
├── models/
│   └── ghostdag_ordering.py   <-- GHOSTDAG blue-set cluster calculator
├── README.md                  <-- Technical paper & specification
└── LICENSE                    <-- MIT License

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Mathematical modeling of DAG partial orders, Kaspa ($KAS) scalability, and high-velocity state transitions.

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