SwarmSys: Decentralized Swarm-Inspired Agents for Scalable and Adaptive
  Reasoning
Authors: name: Ruohao Li, name: Hongjun Liu, name: Leyi Zhao, name: Zisu Li, name: Jiawei Li, name: Jiajun Jiang, name: Linning Xu, name: Chen Zhao, name: Mingming Fan, name: Chen Liang

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.