Kade Heyborne
48c6ddc066
- Complete planning documentation for 5-phase development - UI design specifications and integration - Domain architecture and directory templates - Technical specifications and requirements - Knowledge incorporation strategies - Dana language reference and integration notes
1.6 KiB
1.6 KiB
universal@white ~/D/M/E/A/Personal Assistant> cat ./8GGuKOrooJA_AI-Dual-Manifold-Cognitive-Architecture-Experts-on_transcript.txt | fabric -sp extract_recommendations
- Use dual indexes: dense vectors for concepts and sparse indexes for exact terms.
- Build a multi-layered memory with episodic, semantic, and persona components.
- Model a user's cognitive trajectory from their personal data over time.
- Transform a person's knowledge timeline into a weighted knowledge graph.
- Convert static knowledge graphs into dynamic, gravity well-like manifolds.
- Create a novelty repulsor to push AI reasoning beyond known expertise.
- Construct a second manifold representing collective, domain-specific knowledge.
- Use a braiding processor to merge individual and collective knowledge streams.
- Implement gated fusion to filter out hallucinations and irrelevant noise.
- Move intelligence from parametric model weights to non-parametric external structures.
- Employ multi-agent systems with specialized domain and author agents.
- Optimize for ideas at the intersection of personal and community knowledge.
- Anchor new ideas in both personal history and social reality.
- Use geometric attention and manifold-constrained neural ODEs for stability.
- Ensure exact lexical matching for technical terms to prevent errors.
- Apply rank fusion to combine results from different retrieval methods.
- Linearize complex graph structures for LLM context windows.
- Design AI personas that act as intellectual sparring partners.
- Frame discovery as a dual-constraint optimization problem.
- Leverage tools like GraphRAG for advanced reasoning over knowledge graphs.