What is the purpose of the 69-skill random walk?

It is a sampling methodology that uses random walks on Ramanujan expanders to efficiently traverse a 181-skill manifold, identifying where obstructions block global consistency.

How does this skill help with World Extractable Value (WEV)?

By identifying and resolving topological obstructions, it reduces coordination loss, effectively driving WEV toward zero to optimize global system performance.

What is an 'obstruction' in this context?

In topological learning, an obstruction is a local element or 'H⁰ generator' that prevents individual sections of a system from being glued together into a globally consistent and harmonious whole.

How does GF(3) conservation ensure system stability?

It assigns a 'trit' value (-1, 0, or +1) to each skill. By ensuring that skill triads sum to zero mod 3, the system maintains compositional coherence and mathematical balance.

Topological Obstruction Learning

Name: Topological Obstruction Learning
Author: plurigrid

byplurigrid

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Data Science & ML

Identifies and resolves global consistency obstructions in topological AI systems using Čech cohomology and GF(3) balancing.

About

The Topological Obstruction Learning skill provides a sophisticated mathematical framework for validating the global coherence of localized AI skill patches. By treating inconsistencies as H⁰ generators (obstructions), it enables developers to detect where logic or data flows break across complex manifolds. It leverages random walk sampling on Ramanujan expanders and GF(3) trit conservation to ensure optimal mixing and verification of skill triads, making it an essential tool for building robust, topologically-aware Artificial Superintelligence (ASI) and complex multi-agent coordination systems.

Key Features

Automated identification of unbalanced cells in 23³ grid architectures
Random walk sampling on Ramanujan expanders for optimal manifold mixing
GF(3) trit conservation validation (-1, 0, +1) for skill triads
Spectral awareness and World Extractable Value (WEV) computation
2 GitHub stars
Čech H⁰ cohomology-based obstruction detection for global consistency

Use Cases

Balancing AI skill manifolds to eliminate coordination loss (WEV)
Verifying global consistency in large-scale multi-agent skill systems
Mapping complex infrastructure and data orchestration via topological verification

About

Key Features

Automated identification of unbalanced cells in 23³ grid architectures
Random walk sampling on Ramanujan expanders for optimal manifold mixing
GF(3) trit conservation validation (-1, 0, +1) for skill triads
Spectral awareness and World Extractable Value (WEV) computation
2 GitHub stars
Čech H⁰ cohomology-based obstruction detection for global consistency

Use Cases

Balancing AI skill manifolds to eliminate coordination loss (WEV)
Verifying global consistency in large-scale multi-agent skill systems
Mapping complex infrastructure and data orchestration via topological verification