Wisdom Of The (AI) Crowd: Investigating Artificial Swarm Intelligence In Large Language Models
arXiv:2606.31404v1 Announce Type: new Abstract: Human swarm intelligence demonstrates remarkable collective accuracy but faces scalability constraints in cost, coordination, and time. We investigate whether large language models (LLMs) can approximate swarm intelligence effects through artificial swarms, addressing a critical gap in understanding AI-based aggregation mechanisms. We conducted a controlled experiment with 960 manually executed prompts across three proprietary models (GPT-5, Gemini 2.5 Pro, Claude Sonnet 4.5), testing intra-model sampling and inter-model aggregation on eight esti
The rapid advancement and accessibility of large language models (LLMs) have created an immediate opportunity to explore novel applications like artificial swarm intelligence, addressing existing limitations of human swarms.
This research provides a new pathway to overcome scalability constraints in collective intelligence by leveraging AI, potentially transforming decision-making processes and resource allocation in complex systems.
The understanding of AI's capability to aggregate intelligence effectively changes, hinting at future systems that can mimic collective human accuracy without human-level operational overhead.
- · AI developers
- · Organizations requiring scalable collective intelligence
- · LLM providers
- · Traditional collective intelligence platforms (due to scalability limitations)
Companies will begin to experiment with AI-powered swarm intelligence for tasks requiring high accuracy aggregation.
This could lead to new types of decision-making software that leverage artificial swarms for rapid, data-driven consensus.
The concept of 'collective intelligence' may shift from human-centric to AI-centric, influencing strategic planning and problem-solving across industries.
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Read at arXiv cs.AI