🌐 Cross-Domain Discovery Engine

Discovering Connections Across Domains of Human Knowledge

Cross-Domain Discovery Engine is an experimental AI research platform focused on discovering meaningful relationships between different domains of knowledge using specialist language models, structured memory, and hybrid retrieval.

Unlike traditional Retrieval-Augmented Generation (RAG) systems that answer questions within a single corpus, the Cross-Domain Discovery Engine explores connections between domains to assist scientific discovery, interdisciplinary research, and hypothesis generation.

The long-term goal is to build an AI system capable of identifying unexplored relationships across disciplines while leveraging Project Mnemosyne as its persistent memory layer.

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Why?

Modern AI systems retrieve information extremely well.

However, they generally retrieve within a single domain.

A physics question searches physics.

A biology question searches biology.

A mathematics question searches mathematics.

Real scientific breakthroughs often emerge between disciplines.

Cross-Domain Discovery Engine aims to build an AI system capable of discovering those hidden connections.

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Objectives

The project aims to:

• Build specialist knowledge bases for multiple scientific domains.
• Route questions to the most relevant domain experts.
• Retrieve information across multiple domains simultaneously.
• Detect unexplored regions within knowledge representations.
• Generate cross-domain hypotheses for further investigation.
• Serve as a research platform for AI-assisted scientific discovery.

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Non-Goals

Cross-Domain Discovery Engine is not:

• A chatbot
• A generic RAG application
• A replacement for foundation models
• A scientific fact generator
• A fully autonomous research scientist

Its purpose is to assist researchers by discovering promising connections between disciplines.

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Core Philosophy

Retrieval finds knowledge. Discovery finds relationships.

The system is designed around four guiding principles:

1. Knowledge is distributed across domains.
2. Discovery emerges from connecting disciplines.
3. Routing should be modular and specialist-driven.
4. Memory and reasoning should remain separate systems.

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Relationship to Project Mnemosyne

Project Mnemosyne serves as the persistent memory layer for the Cross-Domain Discovery Engine.

Responsibilities are intentionally separated.

Project Mnemosyne

• Document ingestion
• Metadata management
• Embeddings
• Hybrid retrieval
• Knowledge graph
• Long-term memory

Cross-Domain Discovery Engine

• Specialist routing
• Multi-domain retrieval
• Cross-domain reasoning
• Knowledge interpolation
• Gap detection
• Hypothesis generation

This separation allows both projects to evolve independently while remaining tightly integrated.

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High-Level Architecture

                    User
                      │
                      ▼
               Query Analysis
                      │
                      ▼
              Specialist Router
                      │
      ┌───────────────┼───────────────┐
      ▼               ▼               ▼
 Physics KB      Biology KB      Mathematics KB
      │               │               │
      └───────────────┼───────────────┘
                      ▼
             Cross-Domain Retrieval
                      ▼
             Discovery Engine
                      ▼
             Hypothesis Generator
                      ▼
                    LLM
                      ▼
                  Response

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Current Status

🚧 Active Research

Current Phase:

Phase 0 — Foundation

Version:

v0.1.0-alpha

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Technology Stack

Layer	Technology
Language	Python
Vector Search	FAISS
Embeddings	Sentence Transformers (MiniLM)
LLM	Ollama (Qwen3 8B)
Notebook	Jupyter
Future Vector DB	Qdrant
Future Knowledge Graph	Neo4j

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Research Roadmap

✅ Phase 0 — Foundation

• Keyword-based routing
• Specialist knowledge bases
• FAISS semantic search
• Local LLM integration

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🚧 Phase 1 — Specialist Routing

• Semantic router
• Embedding-based routing
• Domain classification

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⏳ Phase 2 — Cross-Domain Retrieval

• Simultaneous retrieval from multiple domains
• Multi-domain reranking
• Knowledge fusion

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⏳ Phase 3 — Discovery Engine

• Gap detection
• Cross-domain interpolation
• Knowledge-space exploration
• Discovery candidate ranking

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⏳ Phase 4 — Research Platform

• Reinforcement learning for routing
• Representation learning
• Scientific hypothesis generation
• Discovery evaluation framework

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Documentation

Detailed documentation is available inside the docs/ directory.

• Vision
• Architecture
• Research Notes
• Routing
• Knowledge Space
• Gap Detection
• Topology
• Roadmap
• Architecture Decision Records

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Engineering Principles

Cross-Domain Discovery Engine is developed from first principles.

Understand
      ↓
Research
      ↓
Design
      ↓
Prototype
      ↓
Implement
      ↓
Experiment
      ↓
Benchmark
      ↓
Document
      ↓
Commit

Whenever possible:

• Understand the underlying mathematics before implementation.
• Build simple systems before introducing complexity.
• Separate experimentation from production architecture.
• Treat every stage as reproducible research.

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Getting Started

# Clone repository
git clone https://github.com/Ultronious/Cross_Domain_Discovery_Engine.git

cd Cross_Domain_Discovery_Engine

# Create virtual environment
python -m venv .venv

# Activate

# Windows
.\.venv\Scripts\Activate.ps1

# Linux / macOS
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

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Long-Term Vision

The long-term ambition of the Cross-Domain Discovery Engine is not simply to answer questions.

Instead, it seeks to assist researchers by identifying meaningful connections that remain hidden across disciplinary boundaries.

As AI memory systems mature through Project Mnemosyne, the Cross-Domain Discovery Engine will evolve into a platform capable of exploring the geometry of knowledge itself—combining retrieval, representation learning, and structured reasoning to support scientific discovery.

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License

MIT License

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Building experimental AI systems that explore how knowledge can be connected, organized, and discovered across disciplines.