@Article{cmc.2026.084480,
AUTHOR = {Yazhi Zheng, Xiaolong Cui, Xin Wang, Xuanzhu Sheng},
TITLE = {Research on an Emergence Mechanism in Large Language Models for Command and Decision-Making},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/28048},
ISSN = {1546-2226},
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 , , Cohen’s ) and Standard-CoT (, ) in composite performance. PRM-guided Best-of-N selection further improves performance by 5.8% over single-sample CoCD (, ), 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: 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.},
DOI = {10.32604/cmc.2026.084480}
}