A Neuro-Symbolic Multi-Agent Approach to Legal-Cybersecurity Knowledge Integration
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Abstract (arXiv)
The growing intersection of cybersecurity and law creates a complex information space where traditional legal research tools struggle to deal with nuanced connections between cases, statutes, and technical vulnerabilities. This knowledge divide hinders collaboration between legal experts and cybersecurity professionals. To address this important gap, this work provides a first step towards intelligent systems capable of navigating the increasingly intricate cyber-legal domain. We demonstrate promising initial results on multilingual tasks.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 27 references that are in the map. First posted on arXiv 27 Oct 2025.
Builds on
8 of 27 references.
References · 8
- 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
- 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
- 2017Deep Reinforcement Learning that Matterscited by 727
- 2023JudgeLM: Fine-tuned Large Language Models are Scalable Judgescited by 351
- 2017Beam Search Strategies for Neural Machine Translationcited by 232
- 2023Machine Learning for Synthetic Data Generation: A Reviewcited by 118
- 2024CyberMetric: A Benchmark Dataset based on Retrieval-Augmented Generation for Evaluating LLMs in Cybersecurity Knowledgecited by 55
- 2018Using Deep Neural Networks to Translate Multi-lingual Threat Intelligencecited by 4
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Citing papers per year: 2026: 1.
1 paper.