[2603.24676] When Is Collective Intelligence a Lottery? Multi-Agent Scaling Laws for Memetic Drift in LLMs

Computer Science > Artificial Intelligence arXiv:2603.24676 (cs) [Submitted on 25 Mar 2026] Title:When Is Collective Intelligence a Lottery? Multi-Agent Scaling Laws for Memetic Drift in LLMs Authors:Hidenori Tanaka View a PDF of the paper titled When Is Collective Intelligence a Lottery? Multi-Agent Scaling Laws for Memetic Drift in LLMs, by Hidenori Tanaka View PDF HTML (experimental) Abstract:Multi-agent systems powered by large language models (LLMs) are increasingly deployed in settings that shape consequential decisions, both directly and indirectly. Yet it remains unclear whether their outcomes reflect collective reasoning, systematic bias, or mere chance. Recent work has sharpened this question with naming games, showing that even when no individual agent favors any label a priori, populations rapidly break symmetry and reach consensus. Here, we reveal the mechanism by introducing a minimal model, Quantized Simplex Gossip (QSG), and trace the microscopic origin of this agreement to mutual in-context learning. In QSG, agents maintain internal belief states but learn from one another's sampled outputs, so one agent's arbitrary choice becomes the next agent's evidence and can compound toward agreement. By analogy with neutral evolution, we call this sampling-driven regime memetic drift. QSG predicts a crossover from a drift-dominated regime, where consensus is effectively a lottery, to a selection regime, where weak biases are amplified and shape the outcome. We deriv...