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📚 Research Library ​

Cognitive Layer for Knowledge Management ​

The Mnemoverse Research Library is more than a collection of sources - it's a cognitive layer that transforms raw information into structured knowledge. Our library operates on the principle of separating Knowledge (curated sources) from Experience (usage patterns), creating a protected cognitive space for research.

🧠 The Cognitive Layer Concept ​

Our library implements a three-tier architecture:

  • 🔍 Multi-Modal Access: SQL queries, vector search, hyperbolic distance, full-text search
  • 🛡️ Quality Control: AI-powered validation through our librarian assistant Ala
  • 📈 Experience Layer: Learning from research patterns without polluting the knowledge base

This contains 300+ carefully curated research sources covering artificial intelligence, machine learning, hyperbolic neural networks, multi-agent systems, and consciousness research. All sources are validated and enhanced with semantic metadata.

Available through MCP Protocol for seamless AI integration.

Last updated: 2025-Jul-28 ​

✅ Verified High-Quality Sources ​

manually verified sources with full abstracts and analysis

🤖 Multi-Agent Systems & Collective Intelligence ​

Biological Foundations ​

Technical Implementation ​

Industry Applications ​

📐 Hyperbolic Geometry & Embeddings ​

Core Research ​

Fundamental Hyperbolic Geometry Research ​

Foundational Papers ​

  • Poincaré embeddings for learning hierarchical representations - NeurIPS 2017 - Nickel, M., & Kiela, D. - Classic work introducing hyperbolic space (Poincaré ball) for modeling hierarchies. Uses the same geometry for embedding hierarchical concepts as in Mnemoverse.

  • Hyperbolic neural networks - NeurIPS 2018 - Ganea, O. E., Bécigneul, G., & Hofmann, T. - Extends Poincaré embedding to full neural networks with hyperbolic distance and operations. Used similarly to attention diffusion in Mnemoverse.

  • Poincaré GloVe: Hyperbolic Word Embeddings - arXiv 2019 - Tifrea, A., Bécigneul, G., & Ganea, O. - Generalizes GloVe to hyperbolic geometry. Semantic relationships in text are modeled as curvature, as in Mnemoverse axiom A2.

Theoretical Foundations ​
Geometric Deep Learning ​

  • Geometric deep learning: going beyond Euclidean data - IEEE Signal Processing Magazine 2017 - Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. - Survey of geometric methods in deep neural networks. Justifies the use of metrics and manifolds, as in the attention theory in A2.

  • Hyperbolic Image Embeddings - CVPR 2020 - Khrulkov, V., et al. - Embedding images in hyperbolic geometry, including attention. Multimodal ideas from section 8.2 of Mnemoverse.

Graph Neural Networks ​
  • Hyperbolic Graph Neural Networks - arXiv 2019 - Liu, Q., Nickel, M., & Kiela, D. - Graph GNNs in hyperbolic space. Supports ideas of dynamic cognitive geometry.
Multi-Scale Methods ​
Latent Space Analysis ​
Geometry and Memory ​
Hyperbolic Attention and Visual Memory ​

🎯 UMAP & Dimensionality Reduction ​

Technical Documentation ​

🚀 GPU-Native Graph Processing ​

Performance Analysis ​

🧠 AI Agent Memory & Safety ​

Recent Research ​

Memory Theory and Navigation

🔬 Biological Networks & Evolution ​

Network Evolution

🔄 Manifolds and Generative Models ​

Variational Autoencoders in Curved Spaces

💰 Cost Analysis & Limitations ​

GraphRAG Economics

📐 Theoretical Foundations: Information Geometry & Metrics ​

Information Geometry

Cognitive Structure Learning

🧠 AGI Research & Development (2024-2025) ​

Major AGI Organizations & Roadmaps

AGI Architecture & Capabilities

Memory & Learning Systems

Multi-Agent & Collective Intelligence

Knowledge Representation & Reasoning

Geometric & Spatial Reasoning

Emergent Capabilities & Self-Organization

AGI Safety & Alignment

AGI Funding & Development Timeline

Explore related documentation:

Bibliography ​