Research on an Emergence Mechanism in Large Language Models for Command and Decision-Making

Abstract

: Large Language Models (LLMs) currently lack the robust command and decision-making (C&D) capabilities essential for the command and control domain. To address this critical gap, this paper proposes an emergence mechanism that integrates a domain-specialized Chain of Thought (CoT) framework with a Process Reward Model (PRM)-inspired evaluation and inference-time optimization paradigm. We construct a novel Chain of Command and Decision (CoCD) framework, a C2-specific CoT structure with contextual persistence, knowledge accumulation, and a human-in-the-loop feedback loop, and define a four-dimensional PRM-inspired evaluation framework for process-level assessment of C&D reasoning. Experimental evaluations on 40 C&D scenarios of varying complexity demonstrate that the CoCD framework significantly outperforms direct prompting (Mann–Whitney U = 1314, p < 0.0001, Cohen’s d = 1.340) and Standard-CoT ( p = 0.005, d = 0.606) in composite performance. PRM-guided Best-of-N selection further improves performance by 5.8% over single-sample CoCD ( p < 0.001, d = 0.855), providing direct empirical evidence for the utility of process-aware reward signals at inference time. CoCD’s structural advantage is greatest in high-uncertainty, structurally ambiguous scenarios (Level 3 gap: + 0.925 points), revealing a complexity-type effect that informs the deployment scope of structured CoT frameworks. These findings provide empirical support for domain-specialized structured reasoning and process-level evaluation as foundations for future RL-based C&D capability development in LLMs.