arXiv:2606. 09724v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become a standard architectural response to unreliability in legal AI, yet high-profile failures, including fabricated citations submitted to courts and anachronistic legal content presented as current, continue to appear across jurisdictions.
By Hudson de Martim
arXiv:2606. 15646v1 Announce Type: new Abstract: Large Language Models (LLMs) have transformed natural language processing, but their lack of interpretable reasoning and tendency to hallucinate pose significant challenges for legal applications.
By Deepa Tilwani, Yash Saxena, Ankur Padia, Srinivasan Parthasarathy, Manas Gaur
arXiv:2506. 07853v5 Announce Type: replace Abstract: Representing the temporal evolution of legal norms is a critical challenge for automated processing.
By Hudson de Martim
LEGO is a dual‑module framework that combines a Legal Expert GraphRAG system with an expert Chain‑of‑Thought approach to enhance complex legal reasoning. The GraphRAG component uses an expert‑annotated civil code graph and a greedy normative‑coverage retrieval algorithm to extract relevant provision subgraphs, while the Chain‑of‑Thought module structures retrieved provisions and case facts into a Provision‑Fact‑Conclusion reasoning flow. Using a Qwen3‑8B backbone, LEGO achieves 40.53% exact‑match accuracy on LawExamQA_Civil, surpassing baseline RAG and CoT models and matching larger models on multi‑hop and open‑ended benchmarks, with ablation studies confirming the complementary benefits of both modules.
By Qingjing Chen, Junkai Zhang, Shaochun Wang, Jiahao Ding, Siyuan Zheng, Yukun Yan, Zhi Zheng, Antonino Rotolo, Yun Liu, Weixing Shen
arXiv:2609.23083v1 Announce Type: new
Abstract: The distinction between the spirit and letter of the law is a central issue across research and everyday life, and a growing concern for building safe,...
By Peng Qian, Andrew Li, Sam Chen, Sonia K. Murthy, Yonatan Belinkov, Tomer D. Ullman
arXiv:2603. 05171v2 Announce Type: replace-cross Abstract: This Guideline presents a systematic and operationalizable annotation framework for representing legal argumentation structures in judicial decisions.
By Kun Chen, Xianglei Liao, Kaixue Fei, Yi Xing, Xinrui Li