How does this skill use GF(3) logic?

It uses Galois Field 3 (mod 3) arithmetic to ensure that skill triads are conserved, maintaining compositional coherence across the system.

What is a Reafference Loop in this context?

It is a cybernetic process where an agent predicts an outcome, executes an action, observes the sensation, and validates the match to update its internal model.

What are the four quadrants of classification?

States are mapped based on whether they are Colorable or Derangeable, ranging from Q1 (Optimal) to Q3 (Deadlock states).

What is World Extractable Value (WEV)?

WEV is a metric defined as the sum of coordinated outcomes minus coordination costs, used to measure the efficiency of system interactions.

WEV Verification & Topological Validation

Name: WEV Verification & Topological Validation
Author: plurigrid

byplurigrid

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Data Science & ML

Validates World Extractable Value (WEV) and topological system states using GF(3) logic and cybernetic reafference loops.

About

The WEV Verification skill provides a specialized framework for measuring and validating World Extractable Value (WEV) within complex coordination systems. It utilizes topological quadrant classification to distinguish between optimal, colorable, and derangeable states while ensuring compositional coherence through GF(3) triad conservation. This skill is particularly useful for developers building multi-agent systems, decentralized coordination protocols, or artificial life simulations that require rigorous state-matching and efficient consensus mechanisms like Proof-of-Frog.

Key Features

Supports Proof-of-Frog consensus status monitoring
Integrates with category theory structures (Comod P) for scientific modeling
Classifies system states into colorable and derangeable quadrants
2 GitHub stars
Calculates WEV efficiency gains using GF(3) conservation formulas
Implements cybernetic reafference loops for learning agent validation

Use Cases

Optimizing coordination costs in decentralized multi-agent environments
Verifying state transitions in topological graph networks
Building self-correcting feedback loops for autonomous learning agents

About

Key Features

Supports Proof-of-Frog consensus status monitoring
Integrates with category theory structures (Comod P) for scientific modeling
Classifies system states into colorable and derangeable quadrants
2 GitHub stars
Calculates WEV efficiency gains using GF(3) conservation formulas
Implements cybernetic reafference loops for learning agent validation

Use Cases

Optimizing coordination costs in decentralized multi-agent environments
Verifying state transitions in topological graph networks
Building self-correcting feedback loops for autonomous learning agents