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MIT License
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Copyright (c) 2025 Syed Abdur Rehman Ali
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ARTIFICIAL NEURAL MESH (ANM)
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:
- 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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