LLM-Assisted Iterative Evolution with Swarm Intelligence Toward SuperBrain
This work addresses the problem of scalable and explainable collective AI for applications like autonomous systems and knowledge management, presenting a novel conceptual framework rather than incremental improvements.
The paper tackles the challenge of developing collective intelligence by proposing a SuperBrain framework that co-evolves large language models (LLMs) and human users through iterative refinement and swarm coordination, resulting in an emergent meta-intelligence capable of abstraction and self-improvement, with initial implementations in UAV scheduling and keyword filtering.
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.