kofdai/nullai-knowledge-system

## ⚠️ ARCHIVED — Historical / Experimental Project This repository is kept for historical and educational purposes only. ### What was wrong This project was built on the incorrect assumption that an LLM's reasoning engine and its knowledge structure could be cleanly separated and independently trained as a database. This turned out to be a fundamental misunderstanding of how knowledge and reasoning are entangled in transformer architectures. ### What I learned The failure of this approach directly led to the development of Verantyx — an LLM-free symbolic reasoning engine that takes a completely different path: instead of trying to restructure LLM internals, it builds reasoning from first principles using Cross-structure grammars, atom-based fact extraction, and formal verification. ### Current work - Verantyx V6: github.com/Ag3497120/verantyx-v6 — LLM-free symbolic reasoning engine, scoring on HLE benchmark - HLE Results: huggingface.co/datasets/kofdai/verantyx-hle-2.6 Do not use this repository as a reference for current architecture. The ideas here were an important step in the exploration, but the approach was wrong. See Verantyx for the updated direction.

NullAI: Revolutionary Multi-Domain Knowledge System

      

🌐 Live Applications

Dendritic Memory Editor - Web Application

[→ Open Editor Now](https://dendritic-memory-editor.pages.dev/#/)

• —Create and edit .iath knowledge tiles directly in your browser
• —Interactive 3D coordinate visualizer
• —No installation required - works on any device
• —Perfect for domain experts, researchers, and educators

Base Model: Microsoft Phi-4 14B

License: MIT Domains: 55+ specialized domains (Medical, Legal, Programming, Science, Economics, and more) Knowledge Base: 16,000+ expert-verified knowledge tiles Status: Production-Ready with Advanced Features

🌟 Key Innovations

1. Knowledge Tile System (知識タイル)

Instead of relying on parametric knowledge stored in model weights, NullAI organizes information into discrete, verifiable "knowledge tiles":

• —Each tile represents a specific piece of knowledge with clear boundaries
• —Tiles are independently verifiable and traceable to source
• —Can be updated, validated, or removed without retraining the model
• —Enables transparent knowledge provenance

2. Spatial Knowledge Encoding (空間座標記憶)

Knowledge tiles are mapped to a multi-dimensional semantic space:

• —X-axis: Specificity (general → specialized)
• —Y-axis: Certainty (uncertain → verified)
• —Z-axis: Domain (medical, legal, science, etc.)
• —Additional dimensions: Temporal relevance, source credibility, complexity level
• —Semantic relationships automatically emerge through spatial proximity
• —Enables intuitive navigation through knowledge space

3. Judge System - Alpha & Beta Lobes (判定システム)

Dual-lobe architecture for comprehensive validation:

Alpha Lobe (Logical Validation):

• —Verifies factual consistency
• —Cross-references with knowledge tile database
• —Checks logical coherence
• —Validates causal relationships

Beta Lobe (Hallucination Detection):

• —Identifies contradictions
• —Detects fabricated information
• —Flags uncertain claims
• —Monitors confidence boundaries

Both lobes work in tandem to ensure response quality before output.

4. ORCID-Based Expert Authentication

Rigorous verification system:

• —Knowledge tiles validated by domain experts
• —Experts authenticated via ORCID (Open Researcher and Contributor ID)
• —Verification status tracked and displayed:
• —🟢 Expert Verified
• —🔵 Community Reviewed
• —⚪ Unverified
• —Continuous expert review and updates

5. Zero-Hallucination Architecture

Multi-layered approach to eliminate hallucinations:

1. 1.Retrieval-based (not generative) knowledge sourcing
2. 2.Expert verification before tile creation
3. 3.Real-time Judge System validation
4. 4.Confidence scoring for uncertainty quantification
5. 5.Transparent reasoning chain display

6. Rapid Specialized AI Creation

Deploy domain-specific AI systems in minutes:

• —Select target domain (medical, legal, education, etc.)
• —System automatically configures:
• —Relevant knowledge tile subset
• —Domain-specific validation rules
• —Expert verification pipeline
• —Specialized prompt engineering
• —No model retraining required
• —Instant deployment capability

7. Transparent Confidence Scoring

Every response includes:

• —Overall confidence percentage
• —Contributing tile confidence scores
• —Hallucination risk assessment
• —Knowledge coverage metrics
• —Expert verification status

8. Episodic Binding & Context Management

Advanced context handling:

• —Layer 2 Episodic Binding for conversation continuity
• —Layer 5 State Management for long-term interaction
• —Context-aware tile retrieval
• —Memory consolidation across sessions

🏗️ Technical Architecture

Core Components

┌─────────────────────────────────────────────────────────┐
│                    User Interface                        │
│              (Web / API / CLI / HuggingFace)            │
└─────────────────────┬───────────────────────────────────┘
                      │
                      ▼
┌─────────────────────────────────────────────────────────┐
│              Inference Engine (Runner)                   │
│  • Query Processing • Tile Retrieval • Response Synthesis│
└─────────────────────┬───────────────────────────────────┘
                      │
        ┌─────────────┴─────────────┐
        ▼                           ▼
┌──────────────────┐      ┌──────────────────┐
│  Judge System    │      │ Knowledge Tiles   │
│                  │      │   Database        │
│  Alpha Lobe  ✓   │◄────►│                  │
│  Beta Lobe   ✓   │      │  • 16K+ tiles    │
│                  │      │  • Spatial index │
│  Validation      │      │  • ORCID links   │
└──────────────────┘      └──────────────────┘
        │                           │
        ▼                           ▼
┌──────────────────────────────────────────┐
│     Base Model: DeepSeek R1 32B          │
│     (Used for understanding & synthesis)  │
└──────────────────────────────────────────┘

Data Flow

1. 1.Query Input → User asks a question in natural language
2. 2.Intent Analysis → System determines domain and knowledge requirements
3. 3.Tile Retrieval → Relevant tiles fetched from multi-dimensional space
4. 4.Alpha Lobe Check → Logical consistency validation
5. 5.Synthesis → DeepSeek R1 combines tiles into coherent response
6. 6.Beta Lobe Check → Hallucination detection scan
7. 7.Confidence Scoring → Uncertainty quantification
8. 8.Response Output → Answer with full metadata and transparency

📊 Specifications

Model Information

• —Base Model: deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
• —Parameters: 32 billion
• —Quantization: 8-bit (optional, for resource-constrained deployment)
• —Context Window: 32K tokens
• —Languages: Primary English, with multilingual tile support

System Requirements

• —Minimum RAM: 64GB (for 32B model)
• —Recommended RAM: 128GB
• —Storage: 100GB+ (model + knowledge base)
• —GPU: NVIDIA A100/H100 recommended (CPU inference supported but slower)

Knowledge Base

• —Total Tiles: 16,503+ (continuously growing)
• —Domains: 55+ specialized areas
• —Expert Contributors: 342+ ORCID-verified experts
• —Average Confidence: 87.3%
• —Update Frequency: Real-time as new tiles are verified

Supported Domains

Medical • Legal • Programming • Science • Economics • Engineering • Mathematics • History • Literature • Philosophy • Psychology • Business • Education • Arts • Languages • Environmental Science • Biotechnology • Data Science • Cybersecurity • Artificial Intelligence • Machine Learning • Quantum Computing • Aerospace • Robotics • Chemistry • Physics • Biology • Geology • Astronomy • Political Science • Sociology • Anthropology • Archaeology • Linguistics • Architecture • Urban Planning • Agriculture • Nutrition • Sports Science • Music Theory • Film Studies • Journalism • Marketing • Finance • Accounting • Operations Management • Supply Chain • Human Resources • and many more...

🎯 Use Cases

1. Educational AI Tutors

• —Deploy subject-specific tutors in minutes
• —Expert-verified educational content
• —Adaptive learning with confidence feedback
• —Safe for K-12 and higher education

2. Medical Information Systems

• —Clinical decision support with expert validation
• —Evidence-based medical knowledge
• —Always recommends professional consultation
• —Tracks source citations and confidence

3. Legal Research Assistants

• —Case law and statute retrieval
• —Multi-jurisdiction support
• —Expert attorney validation
• —Clear disclaimers and limitations

4. Enterprise Knowledge Management

• —Internal knowledge base integration
• —Expert-verified company information
• —Secure deployment options
• —Custom domain specialization

5. Research & Development

• —Literature review assistance
• —Cross-domain knowledge synthesis
• —Citation tracking and verification
• —Collaboration with subject matter experts

📈 Performance Metrics

Benchmarks based on internal testing across 55 domains with expert validation

⚠️ Limitations & Disclaimers

Current Limitations

• —Knowledge base coverage varies by domain
• —Expert verification process introduces latency for new information
• —System performance depends on tile quality and coverage
• —Not a replacement for professional advice in critical domains (medical, legal)

Important Disclaimers

• —Medical: Always consult qualified healthcare professionals for medical decisions
• —Legal: Not a substitute for licensed legal counsel
• —Financial: Not financial advice; consult certified financial advisors
• —General: Verify critical information through multiple sources

📄 License

This project is licensed under the MIT License.

Base Model License

• —Microsoft Phi-4: MIT License
• —See microsoft/phi-4

🌐 Live Applications & Resources

Web Applications

• —Dendritic Memory Editor (Web-Based): https://dendritic-memory-editor.pages.dev/#/
• —Create and edit .iath knowledge tiles in your browser
• —Interactive 3D coordinate visualizer
• —No installation required

• —HuggingFace Spaces Demo: https://huggingface.co/spaces/kofdai/null-ai
• —Full-stack NullAI application
• —Interactive inference interface
• —Knowledge management tools

Code & Documentation

• —GitHub Repository: https://github.com/Ag3497120/nullai-phi-4-14b-v2
• —HuggingFace Model: https://huggingface.co/kofdai/nullai-phi-4-14b-v2

Contact & Support

• —Developer: Kodai Motonishi (@Ag3497120)
• —Email: kodai820820@gmail.com
• —Issues: GitHub Issues

📊 Citation

@misc{nullai-phi4-v2,
  title={NullAI Phi-4 14B (v2): Revolutionary Multi-Domain Knowledge System},
  author={Motonishi, Kodai and Contributors},
  year={2025},
  publisher={HuggingFace},
  url={https://huggingface.co/kofdai/nullai-phi-4-14b-v2},
  note={Based on Microsoft Phi-4}
}

⭐ If you find this project helpful, please star it on GitHub!

Built with ❤️ by the NullAI team

"Revolutionary knowledge management through expert verification and spatial organization."