# LLM-Assisted Iterative Evolution with Swarm Intelligence Toward SuperBrain

## TL;DR

This paper introduces the SuperBrain framework that combines large language models, human interaction, evolutionary algorithms, and swarm intelligence to create a scalable, adaptive, and collective form of artificial general intelligence.

## Contribution

It presents a novel architecture integrating LLMs, human users, genetic algorithms, and swarm intelligence to develop a self-improving collective intelligence system.

## Key findings

- Initial implementations in UAV scheduling and keyword filtering demonstrate effectiveness.
- The framework enables dynamic prompt refinement and collective optimization.
- SuperBrain exhibits emergent meta-intelligence capabilities.

## Abstract

We propose a novel SuperBrain framework for collective intelligence, grounded in the co-evolution of large language models (LLMs) and human users. Unlike static prompt engineering or isolated agent simulations, our approach emphasizes a dynamic pathway from Subclass Brain to Superclass Brain: (1) A Subclass Brain arises from persistent, personalized interaction between a user and an LLM, forming a cognitive dyad with adaptive learning memory. (2) Through GA-assisted forward-backward evolution, these dyads iteratively refine prompts and task performance. (3) Multiple Subclass Brains coordinate via Swarm Intelligence, optimizing across multi-objective fitness landscapes and exchanging distilled heuristics. (4) Their standardized behaviors and cognitive signatures integrate into a Superclass Brain, an emergent meta-intelligence capable of abstraction, generalization and self-improvement. We outline the theoretical constructs, present initial implementations (e.g., UAV scheduling, KU/KI keyword filtering) and propose a registry for cross-dyad knowledge consolidation. This work provides both a conceptual foundation and an architectural roadmap toward scalable, explainable and ethically aligned collective AI.