Target value assessment is a critical component of operational decision-making; however, existing methodologies suffer from the rigidity of static models in dynamic battlefield environments and the scarcity of specialized domain-specific data. To address these problems, we propose DECIS, a framework for adaptive decision support based on Retrieval-Augmented Generation (RAG). DECIS operates through a cohesive three-stage architecture centered on two core functions: first, RAG is employed to intelligently match optimal assessment strategies to dynamic contexts, thereby mitigating the challenge of limited military corpora; second, the Large Language Model (LLM) component autonomously generates and refines target lists, ensuring decision recommendations are characterized by high temporal validity and situational awareness. Experimental validation confirms that DECIS reduces the Largest Connected Component to 0.6, compared to 0.75 for PageRank and 0.8 for Betweenness Centrality, providing robust decision support and demonstrating competitive performance against established baselines in dynamic target value assessment.

Tackling the intricacies of network dismantling in complex systems poses significant challenges. This task has relevance across various practical domains, yet traditional approaches focus primarily on singular metrics, such as the number of nodes in the Giant Connected Component (GCC) or the average pairwise connectivity. In contrast, we propose a unique metric that concurrently targets nodes with the highest degree and reduces the GCC size. Given the NP-hard nature of optimizing this metric, we introduce MaxShot, an innovative end-to-end solution that leverages graph representation learning and reinforcement learning. Through comprehensive evaluations on both synthetic and real-world datasets, our method consistently outperforms leading benchmarks in accuracy and efficiency. These results highlight MaxShot’s potential as a superior approach to effectively addressing the network dismantling problem.

Tackling the intricacies of network dismantling in complex systems poses significant challenges. This task has relevance across various practical domains, yet traditional approaches focus primarily on singular metrics, such as the number of nodes in the Giant Connected Component (GCC) or average pairwise connectivity. In contrast, we propose a unique metric that concurrently targets nodes with the highest degree and reduces the GCC size. Given the NP-hard nature of optimizing this metric, we introduce MaxShot, an innovative end-to-end solution that leverages graph representation learning and reinforcement learning. Through comprehensive evaluations on both synthetic and real-world datasets, our method consistently outperforms leading benchmarks in accuracy and efficiency. These results highlight MaxShot’s potential as a superior approach to address the network dismantling problem effectively.