SwarmSys: Decentralized Swarm-Inspired Agents for Scalable and Adaptive Reasoning
This addresses scalability and adaptability issues in multi-agent reasoning for AI researchers and practitioners, representing a novel paradigm rather than an incremental improvement.
The paper tackles the limitations of existing multi-agent frameworks in large language model reasoning by introducing SwarmSys, a decentralized swarm-inspired system that improves accuracy and reasoning stability across symbolic reasoning, research synthesis, and scientific programming tasks.
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.