OpenAI single-agent LLM architecture reduces computational overhead relative to multi-agent orchestration in a simulated mars rover decision-support benchmark
Mars rover missions require decision-support systems that can interpret terrain, telemetry, environmental conditions, and mission objectives under delayed communication with Earth. This study evaluates whether multi-agent orchestration improves simulated Mars rover decision support compared with a single-agent baseline. A controlled benchmark of 100 synthetic mission-inspired rover scenarios was evaluated using OpenAI GPT-4o and GPT-5.5, with five repeated runs per scenario and architecture. Model-facing scenario inputs were separated from evaluator-side labels so that expected actions and hazards were reserved for scoring only. Performance was measured using decision accuracy, exact and substring-based semantic hazard F1, hazard error counts, latency, token usage, scenario-level paired statistical comparisons, and GPT-4o specialist-agent ablations. Across the tested OpenAI configurations, the single-agent architecture showed numerical advantages in decision accuracy and hazard-label alignment, but these decision-quality differences were not consistently significant under scenario-level statistical analysis with Holm-Bonferroni adjustment. The only decision-quality metric remaining significant was GPT-5.5 exact hazard F1, although absolute values were very low. The most reliable difference was computational efficiency: the single-agent architecture required substantially lower latency and token usage than the prompt-defined multi-agent orchestration architecture. Multi-agent orchestration generated broader hazard lists, including plausible non-canonical observations, but did not reliably improve aggregate decision accuracy or hazard F1. These findings suggest that, for short-context, tool-less, static decision-support tasks where all relevant context is available in a single input, multi-agent orchestration should be treated as a cost-bearing design choice rather than an assumed improvement. The study contributes a reproducible architecture-level benchmark for evaluating when LLM-based orchestration is worth its operational cost in mission-inspired workflows.