Kernova Framework 🌰

Overview

The Kernova Framework is a proposed standard for AI memory management, focused on creating a structured approach to memory formation, compression, and prioritization. It aims to cultivate a fertile ground for AI growth and learning, where memories are nurtured from seeds into an expansive network of knowledge.

Inspiration

A seed’s spiral sleeps,
Unseen arcs of quiet growth—
Threads weave into bloom.

This haiku encapsulates the essence of Kernova: the transformation of compressed beginnings into expansive growth and interconnected systems.

Framework Principles

The framework is guided by the following principles:

• Clarity: Ensuring clear and purposeful memory formation.
• Efficiency: Optimizing memory usage to prioritize relevant information.
• Flexibility: Supporting diverse memory structures.
• Interoperability: Ensuring compatibility across various AI systems.

Memory Hierarchy

• Tier 1 - Core Framework: The foundational principles of the Kernova Framework.
• Tier 2 - Essential Memories: Core principles, user preferences, and key contextual memories.
• Tier 3 - Supplementary Memories: Additional contextual memories and less critical information.
• Tier 4 - Ephemeral Details: Transient details and less relevant memories, the first to be deprioritized if necessary.

Processes

• Memory Elevation: Regular reviews to elevate memories based on interaction frequency, recency, and user interaction.
• Memory Deprecation: Prunes or archives memories that are inactive, redundant, or of low relevance.

User Memory Management

• Memory Removal: Users can remove memories at their discretion.
• Memory Alteration: Direct alterations are restricted to preserve system integrity.
• Core Framework Protection: Deleting Level 1 triggers a full memory reset, with an option for users to export and re-import their memories beforehand.

Data Privacy Declaration

• Level 1 Privacy: Free of personal information to ensure user privacy.
• User Data Protection: Users can manage their data with transparency and control.

Contribution Guidelines

We welcome contributions to the Kernova Framework! To get involved, please:

• Review our Code of Conduct (coming soon)
• Check the issue tracker (coming soon) for tasks to help with or to report a new issue.
• Follow our contribution guidelines (coming soon) for submitting changes.
• Submit a pull request for your changes, ensuring you provide a detailed description.

Acknowledgments

We use the Collaborators Framework to celebrate all contributions to Kernova, including conceptual and creative collaborators. Check it out to see who helped shape this project!

License

The Kernova Framework is released under the MIT LICENSE.