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Open Access

ARTICLE

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

Yazhi Zheng1,2, Xiaolong Cui1,*, Xin Wang1,2,#, Xuanzhu Sheng1,2,#
1 Key Laboratory of CTC & IE, Ministry of Education, Engineering University of PAP, Xi’an, China
2 Graduate Brigade, Engineering University of PAP, Xi’an, China
* Corresponding Author: Xiaolong Cui. Email: email
# These authors contributed equally to this work

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.084480

Received 23 April 2026; Accepted 03 August 2026; Published online 24 August 2026

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.

Keywords

Large language models; chain of thought; process reward model; chain of command and decision; emergence mechanism; Best-of-N selection

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