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:

Router‑level pre‑checks
Specialist‑level constraints
Global Verifier (final approval)
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 GitHub: 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

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