SwarmSys: Decentralized Swarm-Inspired Agents for Scalable and Adaptive Reasoning
Ruohao Li, Hongjun Liu, Leyi Zhao, Zisu Li, Jiawei Li, Jiajun Jiang, Linning Xu, Chen Zhao, Mingming Fan, Chen Liang
2025-10-14
Summary
This paper introduces a new way for multiple AI programs, called Large Language Models or LLMs, to work together to solve complex problems. It's called SwarmSys, and it's inspired by how insects like ants or bees cooperate in a swarm.
What's the problem?
Currently, when researchers try to get multiple LLMs to collaborate, they often give each one a specific, unchanging job, or they have a central controller telling everyone what to do. This doesn't work well for really complicated tasks that require a lot of thinking and adapting over time because it limits how easily the system can grow and change its approach.
What's the solution?
SwarmSys uses a system of three different types of 'agents': Explorers who come up with new ideas, Workers who develop those ideas further, and Validators who check the quality of the work. These agents constantly cycle through these roles, interacting with each other. The system also uses a way to track what each agent is good at and what tasks are available, and a 'pheromone' system – like ants use to mark trails – to reinforce successful approaches, all without needing someone to oversee everything.
Why it matters?
This research shows that letting AI agents coordinate themselves, like a swarm, can be a really effective way to build more powerful and flexible AI systems. It suggests that improving how these agents work *together* could be just as important as making the individual AI programs themselves even smarter, potentially leading to significant advances in AI capabilities.
Abstract
Large language model (LLM) agents have shown remarkable reasoning abilities. However, existing multi-agent frameworks often rely on fixed roles or centralized control, limiting scalability and adaptability in long-horizon reasoning. We introduce SwarmSys, a closed-loop framework for distributed multi-agent reasoning inspired by swarm intelligence. Coordination in SwarmSys emerges through iterative interactions among three specialized roles, Explorers, Workers, and Validators, that continuously cycle through exploration, exploitation, and validation. To enable scalable and adaptive collaboration, we integrate adaptive agent and event profiles, embedding-based probabilistic matching, and a pheromone-inspired reinforcement mechanism, supporting dynamic task allocation and self-organizing convergence without global supervision. Across symbolic reasoning, research synthesis, and scientific programming tasks, SwarmSys consistently outperforms baselines, improving both accuracy and reasoning stability. These findings highlight swarm-inspired coordination as a promising paradigm for scalable, robust, and adaptive multi-agent reasoning, suggesting that coordination scaling may rival model scaling in advancing LLM intelligence.