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 $1/c$ ($\beta = 0$), sublinear at $N^\beta/c$ ($0 0.99$; only $(c, \beta)$ shifts. On free-form math, dense peer influence collapses the answer-level regime from sublinear into hard-ceiling; correctness-level fits rema
This research provides a foundational scaling law for multi-agent LLM systems, addressing a critical gap in understanding their performance and efficiency as they become more prevalent.
A strategic reader should care because this law allows for better prediction and optimization of multi-agent LLM system performance, directly impacting development costs, efficiency, and real-world applicability.
The ability to accurately model scaling behavior for multi-agent LLMs enables more effective system design and resource allocation, shifting from empirical trial-and-error to theoretically informed development.
- · AI developers
- · Cloud infrastructure providers
- · Enterprises adopting AI agents
- · Inefficient AI agent deployment strategies
Optimization of multi-agent LLM architectures becomes more precise due to quantifiable scaling predictions.
Reduced operational costs and increased reliability for complex AI agent systems across various industrial applications.
Acceleration of the deployment and integration of AI agents into critical white-collar workflows and services.
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