Single-Agent LLMs vs. Multi-Agent Systems

Computation as the hidden confounder: Most reported multi-agent gains are confounded by increased test-time computation rather than architectural advantages. When reasoning token budgets are held constant, the performance gap disappears or reverses, suggesting that prior comparisons were inadvertently measuring compute scaling rather than coordination benefits.

Information-theoretic foundation: The authors ground their analysis in the Data Processing Inequality, arguing that under a fixed reasoning-token budget with perfect context utilization, single-agent systems are inherently more information-efficient. Distributing reasoning across agents introduces information loss at each handoff.

Benchmark artifacts inflate MAS gains: Testing across Qwen3, DeepSeek-R1-Distill-Llama, and Gemini 2.5, the study identifies significant evaluation artifacts, particularly in API-based budget control for Gemini 2.5, that inflate apparent multi-agent advantages. Standard benchmarks also contain structural biases favoring multi-agent decomposition.

Practical implications for system design: The findings suggest that teams should explicitly control for compute, context, and coordination trade-offs before committing to multi-agent architectures. In many cases, allocating the same token budget to a single agent with richer context yields stronger results at lower system complexity.