There is renewed enthusiasm for agentic architecture based on language models. Some research papers suggest this approach might improve the performance of LLM-b

There is renewed enthusiasm for agentic architecture based on language models. Some research papers suggest this approach might improve the performance of LLM-based applications. The recent paper “More Agents Is All You Need,” authored by Junyou Li, Qin Zhang, Yangbin Yu, Qiang Fu, and Deheng Ye, argues that the performance of large language models (LLMs) scales with the number of agents instantiated. The authors describe a simple sampling-and-voting method that not only complements but also enhances existing, more complex methods used to improve LLMs. The effectiveness of this enhancement correlates with task difficulty. The team conducted extensive experiments across various LLM benchmarks to confirm their findings and to explore the properties that contribute to this phenomenon.