LLM-Assisted Iterative Evolution with Swarm Intelligence Toward SuperBrain

2025-08-30

AI Summary

This paper addresses key challenges in the co-evolution of large language models (LLMs) and humans: limited scalability of collective intelligence, poor interpretability, and difficulty in ethical alignment. To this end, we propose “SuperBrain”, a dynamic collective intelligence framework. Methodologically, it employs “sub-cerebrum” units—individual cognitive dyads—as foundational building blocks, and achieves progressive emergence of “super-cerebrum” (i.e., meta-intelligence at the population level) via bidirectional evolution driven by genetic algorithms, cross-dyad knowledge distillation, and automated prompt evolution. Innovatively, it integrates cognitive memory modeling, multi-objective optimization, and a distributed collaborative architecture to ensure interpretability and ethically grounded controllability. Empirical evaluation on UAV scheduling and keyword filtering tasks demonstrates significant improvements in task performance and effective cognitive synergy. Overall, SuperBrain establishes a novel paradigm for self-evolving, scalable, and trustworthy collective intelligence systems.

Technology Category

• Machine Learning: Evolutionary Learning
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Search and Retrieval-Augmented AI: Large language models for search

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.

Problem

• Dynamic co-evolution between LLMs and human users
• Iterative refinement of prompts and task performance
• Integration of multiple cognitive dyads into emergent meta-intelligence

Innovation

• Dynamic co-evolution between LLMs and humans
• GA-assisted forward-backward iterative evolution
• Swarm Intelligence coordination for multi-objective optimization