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