Large language models (LLMs) have expanded beyond natural language processing into decision-making domains like military strategy and real-time gaming, yet face critical challenges in adversarial environments. Existing methods prove inadequate: free-generation mode lacks structured reasoning for tactical analysis, while chain-of-thought (CoT) generates overly detailed steps causing computational overhead. Additionally, LLMs’ hallucination issues and lack of domain-specific constraints often yield suboptimal decisions in high-stakes scenarios. This study develops the dynamic chain-of-reasoning-and-decision (DCoRD) framework to address these limitations by combining structured reasoning with dynamic output constraints specifically designed for adversarial decision-making. Our objectives are threefold: (1) create an efficient reasoning architecture preserving CoT’s interpretability while eliminating its inefficiencies; (2) develop constraint mechanisms ensuring feasible, effective decisions; and (3) establish performance benchmarks using StarCraft Ⅱ’s complex environment, which provides ideal evaluation metrics for testing LLM-based decision systems under time pressure and competitive conditions. The research aims to bridge the gap between LLMs’ potential and their practical application in professional adversarial scenarios where accuracy, speed, and reliability are paramount.

The DCoRD framework introduces a two-step Markov reasoning architecture comprising state reasoning and strategy reasoning. This architecture is enhanced by a carefully designed dynamic decision options library, which provides standardized semantic units for tactical situation assessment and action selection. Crucially, DCoRD extends the conventional CoT triplet prompt into a quadrilateral structure—integrating input, reasoning framework, dynamic options library, and constrained output—as structured few-shot exemplars. Unlike traditional CoT methods that generate overly detailed intermediate steps, DCoRD strategically constrains outputs to predefined, domain-specific options (e.g., “[Terrain]<High Ground>” or “Attack<Enemy Unit>”), while permitting concise free-form rationales, implementing a soft constraint mechanism adaptable to real-time dynamics. This approach achieves three key improvements: (1) substantial reduction in token consumption, (2) minimized risk of hallucination, and (3) maintained decision interpretability. The framework dynamically adjusts output complexity based on real-time environmental demands, achieving an optimal balance between reasoning depth and operational responsiveness. Comprehensive experiments were conducted on the challenging StarCraft Ⅱ multi-agent challenge (SMAC) platform, testing performance across scenarios of varying complexity (2s_vs_1sc and 2c_vs_64zg). The evaluation compared DCoRD against three established baseline methods: Few-Shot, CoT, and chain-of-draft (CoD), with all methods using identical experimental setups and the same LLM backbone (DeepSeekV3) for fair comparison.

The experimental results demonstrate DCoRD’s significant advantages in both decision quality and operational efficiency. In the particularly demanding 2c_vs_64zg scenario, DCoRD achieved an impressive 50% win rate, representing a 40% absolute improvement over traditional CoT methods. The framework also showed superior tactical effectiveness, increasing average enemy eliminations by 41% (from 36.8 to 51.9 units per match). Detailed analysis revealed that these performance gains stem from DCoRD’s ability to focus computational resources on critical decision factors while filtering out irrelevant reasoning branches. Efficiency metrics were equally strong, with DCoRD reducing token usage by 37.5% (from 333.52 to 208.39 tokens per decision) and decreasing response delay by 14.2% compared to CoT. These improvements were achieved without sacrificing decision transparency, as the structured options framework actually enhanced the interpretability of the model’s reasoning process. Additional analysis of decision patterns showed that DCoRD consistently made better use of terrain advantages and unit positioning compared to baseline methods.

This work establishes that structured output constraints and domain-optimized reasoning frameworks are critical for successful LLM deployment in adversarial environments. The DCoRD methodology represents a significant advance in prompt engineering for decision-making tasks, with several key contributions: (1) a novel two-step reasoning architecture specifically designed for dynamic environments, (2) a flexible options library system that balances expressiveness with computational efficiency by allowing manual design or RAG-based dynamic expansion, and (3) empirical validation confirming that moderate output constraints yield better tactical alignment than lengthy reasoning chains. The framework’s flexible design allows for straightforward adaptation to other high-stakes decision domains including military simulations and emergency response planning. Future research directions will focus on two areas: (1) developing automated methods for expanding the options library, (2) extending the framework to support multi-agent cooperation scenarios.