When Is Collective Intelligence a Lottery? Multi-Agent Scaling Laws for Memetic Drift in LLMs

📰 ArXiv cs.AI

Collective intelligence in multi-agent systems powered by LLMs can be unpredictable and subject to memetic drift, leading to uncertain outcomes

advanced Published 27 Mar 2026

Action Steps

Identify the conditions under which collective intelligence in multi-agent systems breaks symmetry and reaches consensus
Analyze the role of memetic drift in shaping the outcomes of multi-agent systems
Develop strategies to mitigate the effects of memetic drift and improve the reliability of collective intelligence
Apply these strategies to real-world applications of multi-agent systems powered by LLMs

Who Needs to Know This

AI engineers and researchers working on multi-agent systems and LLMs can benefit from understanding the scaling laws for memetic drift to improve the reliability of collective intelligence in their systems

Key Insight

💡 Memetic drift can lead to unpredictable outcomes in multi-agent systems powered by LLMs, highlighting the need for careful analysis and mitigation strategies