https://github.com/abd0r/artificial-neural-mesh

Artificial Neural Mesh (ANM) — A Modular Multi-Model Cognitive Architecture with Controlled Self-Expansion and Web-of-Thought Reasoning.
https://github.com/abd0r/artificial-neural-mesh

ai ai-safety artificial-neural-mesh cognitive-architecture episodic-memory llm multi-agent router verifier web-of-thought

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Artificial Neural Mesh (ANM) — A Modular Multi-Model Cognitive Architecture with Controlled Self-Expansion and Web-of-Thought Reasoning.

• Host: GitHub
• URL: https://github.com/abd0r/artificial-neural-mesh
• Owner: Abd0r
• License: mit
• Created: 2025-11-20T15:36:39.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2025-11-20T19:28:59.000Z (6 months ago)
• Last Synced: 2026-03-01T22:00:24.903Z (3 months ago)
• Topics: ai, ai-safety, artificial-neural-mesh, cognitive-architecture, episodic-memory, llm, multi-agent, router, verifier, web-of-thought
• Homepage: https://doi.org/10.5281/zenodo.17664259
• Size: 339 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: License.txt
  • Citation: CITATION.cff
  • Roadmap: Roadmap.md

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README

          
# ARTIFICIAL NEURAL MESH (ANM)

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.17664259.svg)](https://doi.org/10.5281/zenodo.17664259)

**ORCID:** https://orcid.org/0009-0004-6611-2918

### A Modular, Multi-Model Cognitive Architecture with Controlled Self-Expansion and Web-of-Thought Reasoning

**Version:** 1.0

**Author:** Syed Abdur Rehman Ali

**Date:** November 2025

---

## 📘 Overview

**Artificial Neural Mesh (ANM)** is a proposed cognitive architecture designed to overcome the limitations of monolithic LLM systems. Instead of relying on a single model for every domain, ANM uses a network of **specialized LLMs** coordinated by a Router and safeguarded by a Verifier.

It introduces:

* Modular multi-model intelligence

* Parallel reasoning using *Web‑of‑Thoughts (WoT)*

* Domain-specialized experts

* A Refiner + Verifier pipeline for reliability

* Long‑term episodic memory

* Controlled self‑expansion (safe autonomous fine‑tuning)

ANM is **not AGI**, but a **first practical step** toward flexible, safe distributed intelligence.

---

## 🧠 Architecture Summary

### **1. Router LLM**

* Understands tasks

* Splits into subtasks

* Selects specialists

* Loads memory

* Orchestrates the entire mesh

### **2. Specialist LLMs**

Independently fine‑tuned models specializing in:

* Vision

* Coding

* Logic & Mathematics

* Research

* Planning

* Tool Execution

* Safety Evaluation

### **3. Web‑of‑Thoughts (WoT)**

Parallel multi‑model communication enabling specialists to:

* Exchange reasoning

* Cross‑verify outputs

* Merge logical chains

* Debate and refine

### **4. Refiner**

* Merges outputs from specialists

* Removes contradictions

* Enhances clarity and structure

### **5. Verifier**

The final gatekeeper:

* Ensures logical correctness

* Ensures factual accuracy

* Enforces safety and alignment

* Blocks harmful or incorrect outputs

### **6. Episodic Memory**

Vector‑based memory storing:

* past tasks

* reasoning chains

* images, code, events

* user preferences

### **7. Expansion Engine**

A controlled self‑evolution module:

* Detects missing capabilities

* Builds clean datasets

* Fine‑tunes new specialists

* Integrates them safely

* Requires human approval for additional expansion

---

## 🔒 Safety Framework

ANM includes multi‑layer safety:

1. Router‑level pre‑checks

2. Specialist‑level constraints

3. Global Verifier (final approval)

4. Human‑in‑the‑loop oversight

Hard limits prevent AGI‑like runaway behavior:

* No recursive self‑improvement

* No model weight merging

* No cross‑node or cloud expansion

* Single‑machine constraint

* Memory cannot modify weights

* Strict computational boundaries

---

## 🧩 Use Cases

* Multi‑agent software engineering

* Scientific research & mathematics

* Safe autonomous task agents

* Multimodal analysis (vision + logic + code)

* Personal AI assistants

* Education & tutoring systems

* Robotics reasoning pipeline

* AI architecture experimentation

---

## 📄 Full Paper (PDF)

The full technical manuscript is available here in this repository:

**ARTIFICIAL NEURAL MESH (ANM).pdf** [https://github.com/ra2157218-boop/Artificial-Neural-Mesh/blob/main/ARTIFICIAL%20NEURAL%20MESH%20(ANM).pdf)

It includes the architecture diagrams, flow explanations, safety rules, and future directions.

---

## 📚 Citation

If referencing ANM in research:

Ali, Syed Abdur Rehman. *"Artificial Neural Mesh (ANM): A Modular Multi‑Model Cognitive Architecture with Controlled Self‑Expansion and Web‑of‑Thought Reasoning."* Version 1.0, November 2025.

---

## 🙏 Acknowledgments

This manuscript was edited for clarity with assistance from **GPT‑5.1**.

All architectural concepts, system design, and research ideas are fully authored by **Syed Abdur Rehman Ali**.

---

## 📬 Contact

**Email:** [ra2157218@gmail.com](mailto:ra2157218@gmail.com)

**GitHub:** [https://github.com/ra2157218-boop](https://github.com/ra2157218-boop)

---

## ⭐ Future Research Directions

* Distributed multi‑node mesh architectures

* Hardware‑accelerated WoT communication

* Smarter specialist generation

* Unified multimodal specialists

* Real‑time robotics integration

* Stronger interpretability & safety tools

* Formal verification of the Verifier