Journal of System Simulation
Abstract
Abstract: To address issues such as insufficient intelligence of situational understanding in traditional simulation systems, a situational visual question answering dataset was constructed, and a modular reasoning framework was proposed. The SACoT was built, which, under a zero-shot setting, employed expert prompts to guide the model in task decomposition and multimodal information fusion, generating reasoning chains to enhance semantic cognition and interpretability and offering a scalable solution with low computation cost. Experimental results indicate that SACoT improves task allocation, enables models to focus on query-relevant image details, mitigates the fragmentation of chain-of-thought induced by multi-step reasoning, and reduces long-form text forgetting during the reasoning process. This validates the feasibility of modular analysis for situational reasoning, offering a new approach and pathway for applying AI to battlefield situation simulation fusion and combat decision support.
Recommended Citation
Ji, Hongyuan and Qing, Duzheng
(2026)
"Research on Chain-of-thought Technology for Situational Awareness Based on Modular Reasoning,"
Journal of System Simulation: Vol. 38:
Iss.
2, Article 3.
DOI: 10.16182/j.issn1004731x.joss.25-0984
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol38/iss2/3
First Page
278
Last Page
293
CLC
TP391.9
Recommended Citation
Ji Hongyuan, Qing Duzheng. Research on Chain-of-thought Technology for Situational Awareness Based on Modular Reasoning[J]. Journal of System Simulation, 2026, 38(2): 278-293.
DOI
10.16182/j.issn1004731x.joss.25-0984
Included in
Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Systems Science Commons