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The paper introduces Strategic Tactical Agent Reasoning (STAR) Benchmark, a multi-agent evaluation framework for assessing LLMs in 1v1 zero-sum competitive interactions under both turn-based and real-time settings. STAR evaluates not only win-loss outcomes but also strategic behavior quality using a Strategic Evaluation Suite, revealing a strategy-execution gap. Empirical results demonstrate that reasoning-intensive models excel in turn-based settings, while faster instruction-tuned models perform better in real-time scenarios due to lower inference latency.
LLMs that dominate in strategic reasoning often choke in real-time zero-sum games, revealing a critical strategy-execution gap that current benchmarks miss.
Large Language Models (LLMs) have achieved strong performance on static reasoning benchmarks, yet their effectiveness as interactive agents operating in adversarial, time-sensitive environments remains poorly understood. Existing evaluations largely treat reasoning as a single-shot capability, overlooking the challenges of opponent-aware decision-making, temporal constraints, and execution under pressure. This paper introduces Strategic Tactical Agent Reasoning (STAR) Benchmark, a multi-agent evaluation framework that assesses LLMs through 1v1 zero-sum competitive interactions, framing reasoning as an iterative, adaptive decision-making process. STAR supports both turn-based and real-time settings, enabling controlled analysis of long-horizon strategic planning and fast-paced tactical execution within a unified environment. Built on a modular architecture with a standardized API and fully implemented execution engine, STAR facilitates reproducible evaluation and flexible task customization. To move beyond binary win-loss outcomes, we introduce a Strategic Evaluation Suite that assesses not only competitive success but also the quality of strategic behavior, such as execution efficiency and outcome stability. Extensive pairwise evaluations reveal a pronounced strategy-execution gap: while reasoning-intensive models dominate turn-based settings, their inference latency often leads to inferior performance in real-time scenarios, where faster instruction-tuned models prevail. These results show that strategic intelligence in interactive environments depends not only on reasoning depth, but also on the ability to translate plans into timely actions, positioning STAR as a principled benchmark for studying this trade-off in competitive, dynamic settings.
