Research on Chain-of-thought Technology for Situational Awareness Based on Modular Reasoning

doi:10.16182/j.issn1004731x.joss.25-0984

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.

Key words: foundation model, chain-of-thought, situation reasoning, visual question answering, zero-shot learning

CLC Number:

TP391.9

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.

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推理要素	主要内容示例	主要信源	推理要素	主要内容示例	主要信源
环境条件	天气，气候，地形，设施	Deepstate	时空态势	速度，高度，方位，位移	SCSPI
行动策略	进攻，佯攻，伴攻，防御	Deepstate	攻势类型	钳形，楔形，迂回，游击	Deepstate
装备选择	坦克，导弹，无人机，等	ISW	航迹意图	贸易，监视，挑衅，演习	SCSPI
地区职能	后勤，指控，火炮，战略	ISW

分数	细则
1	没有给出答案或者生成的答案与正确答案所指的事物、选项或情况完全无关
2	生成的答案包括多个猜测，有正确答案或部分正确答案，但也可能提到错误答案
3	生成的答案只包含部分正确答案或仅包含正确答案的一个方面，不包含错误答案
4	生成的答案与正确答案相同

方法	平均单样本推理时间/s
Baseline	0.9
CoT	1.3
MMCoT	8.5
CCoT	9.2
VisCoT	4.4
SACoT	7.8

流程消融	召回率	准确率
SACoT(无描述流程)	81.04	67.11
SACoT(无决策流程)	81.74	65.67
SACoT(无集成流程)	81.67	63.22
SACoT	81.90	67.78