SwarmSys: Decentralized Swarm-Inspired Agents for Scalable and Adaptive
  Reasoning
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.
68ee5ac69b77b5223f66624f
Ruohao Li
68ee5ac69b77b5223f666250
Hongjun Liu
claimed_verified
2025-10-14T14:23:51.257Z
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/avatars/5041573e603225e9de3b689efd1dd499.svg
Liu Hongjun
user
Jan150000
68ee5ac69b77b5223f666251
Leyi Zhao
68ee5ac69b77b5223f666252
Zisu Li
68ee5ac69b77b5223f666253
Jiawei Li
68ee5ac69b77b5223f666254
Jiajun Jiang
68ee5ac69b77b5223f666255
Linning Xu
68ee5ac69b77b5223f666256
Chen Zhao
68ee5ac69b77b5223f666257
Mingming Fan
68ee5ac69b77b5223f666258
Chen Liang
SwarmSys: Decentralized Swarm-Inspired Agents for Scalable and Adaptive
  Reasoning
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.
SwarmSys, a distributed multi-agent framework inspired by swarm intelligence, enhances scalability and adaptability in long-horizon reasoning through specialized roles and self-organizing mechanisms.
SwarmSys distributed multi-agent reasoning swarm intelligence Explorers Workers Validators adaptive agent event profiles embedding-based probabilistic matching pheromone-inspired reinforcement mechanism symbolic reasoning research synthesis scientific programming self-organizing convergence