How does this skill calculate World Extractable Value (WEV)?

It measures the difference in 'mixing time' (τ_mix) and compute costs between uncoordinated solo proof attempts and market-coordinated efforts.

Can I use this for Busy Beaver (BB) problems?

Yes, it specifically includes commands for predicting BB(n) bounds, pricing proofs, and managing $BEAVER token reward calculations.

What is the difference between the LEVIN and LEVITY modes?

LEVIN focuses on rapid convergence and shortest-program compression, while LEVITY focuses on exploration, expansion, and finding new patterns.

What is the primary purpose of Levin-Levity?

Levin-Levity is designed to balance algorithmic search efficiency with exploratory novelty to solve complex problems and extract value from computational proof efforts.

Levin-Levity: Algorithmic Complexity & WEV

Name: Levin-Levity: Algorithmic Complexity & WEV
Author: plurigrid

byplurigrid

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데이터 과학 및 ML

Balances algorithmic complexity with exploratory game theory to optimize proof discovery and extract World Extractable Value (WEV).

소개

Levin-Levity is a specialized framework for Claude Code that integrates Leonid Levin’s universal search and Kolmogorov complexity with game-theoretic 'Levity' to solve complex computational problems. It enables developers and researchers to find the Nash equilibrium between search space compression (convergence) and exploratory novelty, facilitating the creation of $BEAVER prediction markets for undecidable problems like Busy Beaver sequences. By analyzing proof inefficiencies and mixing times, the skill extracts World Extractable Value (WEV), allowing for the precise pricing of proofs and the optimization of distributed cognitive efforts.

주요 기능

Computes Levin and Kolmogorov complexity for program and proof optimization
Supports multi-language proof verification for Lean, Agda, and Move
Automates WEV extraction by analyzing proof inefficiencies and mixing times
2 GitHub stars
Generates reward models for $BEAVER tokenomics based on BB(n) bounds
Calculates Nash equilibria between exploration (Levity) and convergence (Levin)

사용 사례

Optimizing resource allocation for complex algorithmic search and discovery tasks
Designing decentralized prediction markets for undecidable or hard-to-compute problems
Arbitraging information asymmetry in large-scale collaborative proof efforts

소개

주요 기능

Computes Levin and Kolmogorov complexity for program and proof optimization
Supports multi-language proof verification for Lean, Agda, and Move
Automates WEV extraction by analyzing proof inefficiencies and mixing times
2 GitHub stars
Generates reward models for $BEAVER tokenomics based on BB(n) bounds
Calculates Nash equilibria between exploration (Levity) and convergence (Levin)

사용 사례

Optimizing resource allocation for complex algorithmic search and discovery tasks
Designing decentralized prediction markets for undecidable or hard-to-compute problems
Arbitraging information asymmetry in large-scale collaborative proof efforts