AgentPSO: Evolving Agent Reasoning Skill via Multi-agent Particle Swarm Optimization
Multi-agent reasoning has shown promise for improving the problem-solving ability of large language models by allowing multiple agents to explore diverse reasoning paths. However, most existing multi-agent methods rely on inference-time debate or aggregation, which can be vulnerable to incorrect peer influence and biased consensus. Moreover, the agents' reasoning procedures remain static, as they do not accumulate reusable behaviors across tasks. In this paper, we introduce \textbf{AgentPSO}, a particle-swarm-inspired framework for evolving multi-agent reasoning skills. AgentPSO treats each agent as a particle-like reasoner whose state is a natural-language skill and whose velocity is a s...
| Authors | Hyunmin Hwang, Jaemin Kim, Choonghan Kim et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 May 2026 |
| Last updated at source | 10 August 2026 |
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