LLM-Assisted Iterative Evolution with Swarm Intelligence Toward SuperBrain
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arXiv
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| Format: | Preprint |
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2025
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| author | Weigang, Li Brom, Pedro Carvalho Siefert, Lucas Ramson |
| author_facet | Weigang, Li Brom, Pedro Carvalho Siefert, Lucas Ramson |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00510 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM-Assisted Iterative Evolution with Swarm Intelligence Toward SuperBrain Weigang, Li Brom, Pedro Carvalho Siefert, Lucas Ramson Artificial Intelligence Computation and Language 68T99 I.2.11; I.2.8; I.2.6 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. |
| title | LLM-Assisted Iterative Evolution with Swarm Intelligence Toward SuperBrain |
| topic | Artificial Intelligence Computation and Language 68T99 I.2.11; I.2.8; I.2.6 |
| url | https://arxiv.org/abs/2509.00510 |