Towards a Science of Scaling Agent Systems: 260-Config Study Finds Multi-Agent Coordination Yields Diminishing Returns
burny_tech · x · 2026-10-04
A 19-author paper on arXiv (2512.08296) introduces quantitative scaling principles for LLM agent systems.
- Controlled evaluation across 260 configurations: 6 agentic benchmarks, 5 architectures (Single-Agent plus Independent/Centralized/Decentralized/Hybrid multi-agent), and 3 LLM families, with standardized tools, prompts, and compute.
- The predictive model reaches cross-validated R²=0.373 (R²=0.413 with a task-grounded capability metric).
- Key findings: (1) capability saturation — coordination yields diminishing returns once single-agent baselines exceed a threshold; (2) tool-heavy tasks incur multi-agent overhead; (3) architectures without centralized verification propagate more errors.
- Shared alongside a related paper on the Ringelmann Effect, scaling effective team size in multi-agent LLM systems.
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