What are Reafference Proofs?

They are on-chain proofs that verify whether an agent's predicted state matched the observed outcome, facilitating a 'closed-loop' for autonomous agent behavior.

How does GF(3) conservation work in this skill?

It requires that entropy triads (sets of three records) sum to 0 mod 3, ensuring mathematical coherence and balance across the system's state transitions.

What is the Hyperbolic Bulk in this context?

Inspired by AdS/CFT correspondence, the Bulk refers to the interior storage of full entropy records and proofs, while the Boundary represents the observable projections like agent skills and colors.

Which blockchain does this skill support?

This skill is specifically built for the Aptos blockchain and utilizes the Move smart contract language for its on-chain operations.

Hyperbolic Bulk Entropy Manager

Name: Hyperbolic Bulk Entropy Manager
Author: plurigrid

byplurigrid

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Database Management

Manages on-chain GF(3) entropy storage and state mediation on Aptos using the bulk-boundary correspondence principle.

About

The Hyperbolic Bulk skill implements a sophisticated on-chain entropy management system based on the AdS/CFT holographic principle. It allows Claude to interact with Aptos Move contracts to store and verify entropy records, ensuring state consistency through GF(3) conservation laws. By mediating between the 'Bulk' (raw entropy records and proofs) and the 'Boundary' (observable agent actions and skills), this skill provides a mathematically rigorous framework for AI agent state management, combining public randomness (DRAND), cognitive state data (EEG), and on-chain VRF to maintain system integrity.

Key Features

Ergodic mediation between agent observables and system entropy
Automated Reafference Proof generation to verify prediction vs. observation
Integration with DRAND, EEG, and Aptos VRF randomness sources
On-chain entropy storage with GF(3) conservation logic
Aptos Move contract interaction for managing entropy triads
2 GitHub stars

Use Cases

Implementing holographic state management in decentralized autonomous systems
Coordinating multi-agent workflows using conserved GF(3) triads
Verifying the integrity of AI agent decision-making via on-chain entropy proofs

About

Key Features

Ergodic mediation between agent observables and system entropy
Automated Reafference Proof generation to verify prediction vs. observation
Integration with DRAND, EEG, and Aptos VRF randomness sources
On-chain entropy storage with GF(3) conservation logic
Aptos Move contract interaction for managing entropy triads
2 GitHub stars

Use Cases

Implementing holographic state management in decentralized autonomous systems
Coordinating multi-agent workflows using conserved GF(3) triads
Verifying the integrity of AI agent decision-making via on-chain entropy proofs