Research exploring how increasing the number of collaborative AI agents leads to enhanced performance, similar to neural scaling laws where more neurons improve capabilities.

Key Findings:

• Multi-agent collaboration networks (MacNets) organize LLMs using directed acyclic graphs for structured interaction
• Adding more collaborative agents yields performance improvements across engineering tasks
• Various network topologies affect collaboration efficiency and outcomes
• Collective reasoning produces more comprehensive solutions than individual agents

Engineering Impact: This research provides a framework for designing more effective AI collaboration systems, potentially transforming how complex engineering problems are solved through distributed AI cognition.

Scaling Large Language Model-based Multi-Agent Collaboration