The Ringelmann Effect in Multi-Agent LLM Systems: A Scaling Law for Effective Team Size 事件

The Ringelmann Effect in Multi-Agent LLM Systems: A Scaling Law for Effective Team Size arXiv:2606.02646v1 Announce Type: cross Abstract: Inference-time multi-agent LLM scaling lacks a shared unit: counting nominal agents conflates cost with independent evidence. We derive a two-parameter scaling law $R(N) = N_\text{eff}/N = 1/(1+c(N-1)N^{-\beta})$ where the regime exponent $\beta$ classifies any configuration into one of three asymptotic regimes -- hard-ceiling at $1/c$ ($\beta = 0$), sublinea