NEXUS — Cross-Domain Insight Engine

NEXUS is an autonomous cross-disciplinary innovation engine designed to solve complex, high-stakes problems by borrowing structural solutions from unrelated scientific and human domains. Instead of looking at surface-level details, NEXUS identifies the mathematical "essence" of a challenge and maps it to fields like Epidemiology, Mycology, or Swarm Intelligence to generate radical, non-obvious hypotheses.

How It Works

[ USER PROBLEM ] 
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[ DECOMPOSER ] ──────► Identifies Structural Essence (e.g., Propagation, Feedback)
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[ DOMAIN MAPPER ] ────► Matches Essence to 50+ Scientific Domains
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[ SOLUTION HUNTER ] ──► Researches Domain-Specific breakthrough mechanisms
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[ BRIDGE BUILDER ] ───► Translates Domain logic into a Novel Hypothesis
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[ SYNTHESIZER ] ──────► Compiles Final Strategic Report (Markdown/PDF)

Features

• Analogical Reasoning: Goes beyond LLM pattern matching by forcing cross-domain synthesis.
• 50+ Scientific Domains: Hardcoded ontology including Glaciology, Cybernetics, Mycorrhizal Networks, and more.
• Deep Research: Real-time academic search via ArXiv and web intelligence via Tavily.
• Visual Workflow: Real-time progress monitoring through a premium "Deep Space" web interface.
• SSE Streaming: Live updates from the LangGraph agent directly to the browser.
• Strategic Scoring: Automatically ranks solutions based on Novelty vs. Feasibility.

Tech Stack

Getting Started

1. Clone the repo
2. Install dependencies:

   pip install -r requirements.txt

3. Configure Environment: Create a .env file and add:

   GROQ_API_KEY=your_key_here
   TAVILY_API_KEY=your_key_here

4. Launch NEXUS:

   python server.py

5. Access the UI: Open your browser and navigate to http://localhost:8000.

(Note: The legacy Streamlit dashboard is still available in app/streamlit_app.py)

Example Inputs

1. Input: "How do we stop misinformation spreading?"
   • Output: An "Immune System" approach using contact-tracing logic from Epidemiology.
2. Input: "How can we make urban transport more efficient?"
   • Output: A "Slime Mold" decentralized routing algorithm inspired by Physarum polycephalum.
3. Input: "How do we reduce employee burnout?"
   • Output: A "Forest Ecology" strategy based on nutrient-sharing mycorrhizal networks.

Project Structure

nexus-insight-engine/
├── agents/           # Specialized LLM nodes
├── knowledge/        # Domain ontology & vector store
├── graph/            # LangGraph workflow orchestrator
├── tools/            # Web, ArXiv, and Semantic Search tools
├── app/              # Streamlit dashboard
├── .env              # Secrets (ignored)
├── config.py         # Global settings
└── requirements.txt  # Dependencies

Why This Is Different From ChatGPT

While ChatGPT can "act like an expert," it is limited by the patterns it has seen in its training data. NEXUS uses a deliberate analogical reasoning pipeline. It forces the AI to move away from the problem space (e.g., Marketing) and look into a foreign space (e.g., Oceanography). By finding structural isomorphisms between these fields, NEXUS uncovers solutions that standard LLM prompts would never reach because they lack the "structural leap" mechanism.