The paper introduces ComplianceAgent, a legal compliance sub‑agent within the COMPASS multi‑agent framework, which uses a large language model to evaluate texts on a fixed rubric across digital sovereignty, carbon impact, compliance, and ethics. The agent grounds its judgments in a reference legal corpus, and if a text scores at or below a critical threshold, it initiates a corrective chain that includes self‑healing rewrites, a compliance label, an explainability report, and escalation to a human reviewer. The authors evaluate the agent in a Canadian and Quebec context on a corpus of thirteen legal instruments and a hand‑annotated gold standard of fifteen texts, demonstrating perfect separation of conforming versus non‑conforming texts, stable scores under repeated queries, and relevant passage retrieval.
By Soukaina Alaoui Ismaili, Jean-Sebastien Dessureault
arXiv:2610.15179v1 Announce Type: cross
Abstract: Large language models are increasingly deployed in contexts requiring adherence to ethical and legal norms, yet their alignment with internationally...
By Alak Raad (Hamad Bin Khalifa University, Qatar), Noreldin Morssy (Hamad Bin Khalifa University, Qatar), Zeerak Suhail (Hamad Bin Khalifa University, Qatar), Serin Atiani (Princess Sumaya University for Technology, Jordan), Mustafa Jarrar (Hamad Bin Khalifa University, Qatar)
arXiv:2608. 15600v1 Announce Type: new Abstract: The widespread circulation of abusive online content has increased the need for reliable moderation of Chinese social-media text.
By Mingyu Yuan, Shengtao Wen, Lingbing Guo, Zhen Bi, Xiang Chen
arXiv:2608.28593v1 Announce Type: new
Abstract: With the increasing development of AI regulatory frameworks, ensuring that artificial intelligence systems, particularly generative models, operate in...
By Cindy Delage, St\'ephane Canu, Marc D\'ecombas, Jonathan Foureur
Ensuring the alignment of Large Language Models (LLMs) with diverse cultural values is critical for their safe global deployment. However, existing alignment benchmarks are predominantly English-centr...
Safety benchmarks for large language models often assess the risk of a user query, although the outcome of question answering depends on whether the response violates a policy. This distinction is cri...
The paper introduces Legal Rule Induction (LRI), a task that seeks to extract concise, generalizable doctrinal rules from analogous judicial precedents. It presents a reproducible pipeline for constructing LRI datasets and, using Chinese law, releases the first benchmark comprising 5,121 case sets (38,088 court cases) for training and 216 expert‑annotated gold test sets. Experiments show that state‑of‑the‑art large language models struggle with over‑generalization and hallucination, but training on the new dataset significantly improves their ability to capture nuanced rule patterns across similar cases.
By Wei Fan, Tianshi Zheng, Yiran Hu, Zheye Deng, Weiqi Wang, Baixuan Xu, Chunyang Li, Haoran Li, Weixing Shen, Yangqiu Song
arXiv:2608. 11816v1 Announce Type: cross Abstract: State-aligned distortion has been documented in China-origin text-based large language models (LLMs), but whether, and in what form, it arises in multimodal systems has not been systematically examined.
By Guang Yang, Fengchen Liu, Alex Wang, Homa Hosseinmardi, Amir Ghasemian
arXiv:2610.27611v1 Announce Type: cross
Abstract: Ensuring the alignment of Large Language Models (LLMs) with diverse cultural values is critical for their safe global deployment. However, existing a...
By Liping Yang, Yongqi Leng, Jingting Zheng, Yuqi Ren, Shaolin Zhu, Deyi Xiong
arXiv:2606.09389v2 Announce Type: replace
Abstract: As large language models (LLMs) are increasingly applied to real-world legal tasks, evaluating the reliability of their open-ended legal responses...
By Yifan Chen, Haitao Li, Yiran Hu, Kaisong Song, Jun Lin, Yueyue Wu, Qingyao Ai, Min Zhang, Yiqun Liu
arXiv:2609.38260v1 Announce Type: cross
Abstract: Values such as honesty, autonomy, and confidentiality are often regarded as general principles underpinning AI alignment. However, what it means to a...
By Olivia Macmillan-Scott, Mirco Musolesi
arXiv:2609.01210v1 Announce Type: cross
Abstract: Safety benchmarks for large language models often assess the risk of a user query, although the outcome of question answering depends on whether the...
By Rui Yang, Shuang Huang, Junhua Liu, Ziqi Zhao, Qingzhong Yan, Yuhang Sun, Cong Liu, Guoping Hu, Rui Mei, Jing Shao