The paper argues that hallucinations by legal language models should be judged as failures of legal warrant rather than mere factual or citation errors. It defines claim-authority warrant as a context-sensitive relationship between a legal claim and applicable, current authority, and proposes that evaluating warrant can uncover failures missed by traditional accuracy or citation metrics. The authors outline a pilot study, benchmark specifications, and a research agenda to assess whether legal AI systems’ claims are properly licensed by law.
By Maksym Taranukhin, Vered Shwartz
arXiv:2606. 23913v1 Announce Type: new Abstract: This article develops an architecture that creates a formally verifiable reward signal to train legal AI, adapting the LLM proposes, verifier disposes paradigm from mathematical AI to the distinctive demands of law.
By Armin Heydari (Harvard University), Torben Leowald (Columbia University)
arXiv:2606. 16118v1 Announce Type: new Abstract: Large Language Models (LLMs) achieve strong performance on reasoning tasks, but whether this reflects faithful logical inference or heuristic approximation remains unclear.
By Olivia Peiyu Wang, Sanna Wong-Toropainen, Daneshvar Amrollahi, Ryan Bai, Tashvi Bansal, Arush Garg, Leilani H. Gilpin
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
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. 14610v1 Announce Type: new Abstract: Legal reasoning tasks such as legal judgment prediction (LJP) require identifying the temporally correct version of the law governing a case -- a capability we term temporal applicable-law determination.
By Yiqian Huang, Shuyuan Zheng, Qianying Liu, Shaowen Peng, Yuntao Kong, Kotaro Funakoshi, Chuan Xiao, Manabu Okumura, Yang Cao