Jos De Roo

🧠 LLM-to-LLM Dialog via Mixture of Experts (MoE) Architecture

🔷 Overview

This system treats each independently developed LLM as an expert module within a broader MoE-inspired coordination framework. A Router/Orchestrator manages interactions, decides which models are involved per task, and facilitates efficient, modular dialog.

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📐 System Components

1. Router (Coordinator Module)

• Parses the user’s input or the current conversational state.

• Routes queries to the most relevant LLM(s) based on:

  • Intent classification
  • Domain expertise tagging
  • Past performance or accuracy tracking

Think of this like a dispatcher choosing the right specialist.

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2. LLM Experts (Independent Agents)

• Each is a fully trained, standalone LLM with its own knowledge base, training history, and capabilities.
• Experts do not need to be retrained or integrated — only wrapped with an interface that accepts standardized input/output.

Example roles:

• LLM_Legal — law and policy expert
• LLM_Technical — STEM and scientific analysis
• LLM_Conversational — natural summarization and tone handling
• LLM_Ethics — bias, fairness, or philosophical reasoning

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3. Standardized Communication Protocol

Each message exchanged uses a shared format, for example:

{
  "sender": "Router",
  "recipient": "LLM_Technical",
  "message": "Evaluate the feasibility of fusion power before 2040",
  "intent": "analytical_request",
  "context": {
    "previous_statements": [...],
    "confidence_threshold": 0.8
  }
}

This allows models from different vendors or frameworks to interoperate.

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4. Aggregation/Consensus Module

• Compares or merges responses from multiple experts.

• Can perform:

  • Voting or ranking of outputs
  • Conflict detection (e.g., two LLMs disagree)
  • Summarization or clarification prompts to resolve ambiguities

Optionally, this module itself could be a lightweight LLM.

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🔄 Interaction Flow Diagram

User Prompt →
     ↓
 [Router]
     ↓
[Selected LLM Experts] (1-3 per task)
     ↓
[Responses returned]
     ↓
[Aggregator/Consensus Module]
     ↓
[Final Output to User]

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🧪 Example Dialog

User Prompt:

“What are the pros and cons of universal basic income from an economic and ethical perspective?”

Behind the Scenes:

[Router] → Sends prompt to:
  - LLM_Economics
  - LLM_Ethics
  - LLM_Conversational (for summarizing)

[LLM_Economics]: Gives data-based arguments, pros/cons.

[LLM_Ethics]: Analyzes justice/fairness implications.

[LLM_Conversational]: Summarizes both perspectives in user-friendly format.

[Aggregator]: Finalizes and formats the response.

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🔧 Benefits

Benefit	Description
Modularity	LLMs can be swapped or updated without retraining the whole system.
Scalability	You can add more experts over time.
Specialization	Each model can focus on its domain, reducing hallucinations.
Transparency	Responses can be traced to specific experts, aiding interpretability.

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⚠️ Challenges

Challenge	Mitigation
Latency	Parallelize expert calls; use caching
Disagreement between experts	Consensus logic, tie-breaking strategies
Security/sandboxing	Use API-level constraints to isolate LLMs
Standard interface complexity	Define clear schemas and enforce input/output specs