Artificial Intelligence in Law

Neueste Paper

• Evaluation is All You Need: Strategic Overclaiming of LLM Reasoning Capabilities Through Evaluation Design

  Yongfu Zhu, Lin Sun, Jinzhu Wu, Weihong Lin, Xiaoqi Jian, Guangxiang Zhao, Change Jia, Linglin Zhang, Sai-er Hu, Yuhan Wu, Xiangzheng Zhang · 28. September 2026

  Reasoning models represented by the Deepseek-R1-Distill series have been widely adopted by the open-source community due to their strong performance in mathematics, science, programming, and other domains. However, our study reveals that their benchmark evaluation results are subject to significant …

• ARGUS: Role-Aware Event Knowledge Graphs for U.S. Employment-Discrimination Complaints

  Sriram Kannan, Swetha Saseendran, Vishnu Vardhan Reddy Kandi, Leslie Barrett, Madhavan Seshadri, Enrico Santus · 25. September 2026

  U.S. employment-discrimination complaints describe complex event sequences that are not explicitly captured by lexical or embedding-based representations alone. We present ARGUS, a source-grounded pipeline that combines a 5W1H-inspired schema, legal-domain models, and LLM-based structured generation…

• Automated Regulatory Compliance Question Answering in Financial Services with Domain-Adapted Retrieval-Augmented Generation

  Tobias Deu{\ss}er, Abhishek Pillai, Aurelio F. Bariviera, Dhananjay Bhardwaj, Lorenz Sparrenberg, David Berghaus, Christian Bauckhage, Rafet Sifa · 25. September 2026

  Financial institutions operate under dense, frequently amended rulebooks, and answering a compliance question correctly requires not only fluency but verifiable grounding in the authoritative text. Large language models are attractive for this task, yet the models that firms can realistically deploy…

• ContraVis: Evidence-Grounded Visual Analytics for Contradiction Review in Legal Contracts

  Luis Sante, Paula Lima, Mariana Rocha, Jorge Poco · 24. September 2026

  Legal contracts are structurally complex documents in which contradictions may emerge across distant and interconnected provisions. Although large language models (LLMs) improve legal language understanding, contradiction analysis remains a human-centered and evidence-grounded review task. We presen…

• LabourCrew: A Multi-Agent RAG Framework for Trustworthy Adversarial Deliberation and Statutory Reasoning over Labour Law

  Fatema Tuj Johora Faria, Mukaffi Bin Moin, Jubayer Al Mahmud, M. F. Mridha, Md. Alam Hossain · 24. September 2026

  In statutory question answering, every claim must be traceable to evidence, not merely relevant, since unverifiable labour-rights answers carry serious legal consequences. Current systems fall short: single-pass RAG cannot detect insufficient evidence, while multi-agent legal-debate systems treat gr…

• Same Scores, Different Decisions: Evaluating JEV and Language Models for Legal Document Understanding

  Fan Zhang, Yankai Chen, Zhuohan Xie, Yixi Zhou, Sijia Peng, Lei Fan, Xinhua Ji, Cunyuan Zheng, Huangyong Shan, Philip S. Yu, Xue Liu, Yu Chen, Preslav Nakov, Songwei He · 24. September 2026

  Contract inference requires multiple judgments about a shared document, but aggregate accuracy can conceal changes in the individual decisions. Repeated agreement is also insufficient: a model may consistently return the wrong answer. In this paper, we compare Jev with nine language models on Contra…

• Cross-Lingual Legal QA for Vietnamese Labour Law: Retrieval, Translation, and Verifier-Guided Correction

  Nguyen Minh Chi, Mo El-Haj, Nguyen Ha Thanh, Dawn Knight, Paul Rayson · 24. September 2026

  Cross-lingual legal question answering must retrieve statutes across languages while preventing unsupported legal claims. We introduce a bilingual evaluation suite of 231 Vietnamese--English question--answer pairs from Vietnamese labour law. Of these, 75 are additionally annotated for five challengi…

• LexLattice: Multilingual Extractive Summarization via Neural Cellular Automata on Document Hierarchies

  Sujay Uday Rittikar, Sheela Ramanna · 24. September 2026

  Faithfulness is a central concern in legal text summarization, which motivates extractive approaches that select verbatim content traceable to its source. Such methods typically rank paragraphs or other structural units in isolation, yet give little attention to consolidating evidence that is distri…

• LEGO: Synergizing Expert GraphRAG and Expert Chain-of-Thought for Legal Reasoning

  Qingjing Chen, Junkai Zhang, Shaochun Wang, Jiahao Ding, Siyuan Zheng, Yukun Yan, Zhi Zheng, Antonino Rotolo, Yun Liu, Weixing Shen · 24. September 2026

  Large language models are increasingly applied to high-risk domains such as law, yet complex legal reasoning remains limited by two structural challenges. First, existing RAG and GraphRAG methods emphasize lexical or semantic similarity while overlooking normative relations among legal provisions. S…

• Classifying Interpretive Canons at the Sentence Level: A Benchmark from the German Federal Constitutional Court

  Felix Ringe · 24. September 2026

  Judicial reasoning remains challenging for large language models (LLMs) to analyze. This paper contributes a sentence-level benchmark for evaluating the ability of LLMs to classify interpretive canons as articulated by Larenz in the tradition of Savigny. Our contributions are threefold. First, we op…

• Mining Legal Arguments in U.S. Corporate Case Law

  Luis Brena, William Jurayj, Gregory Deyesu, Zaid Al-Huneidi, Andrew Blair-Stanek, Benjamin Van Durme · 23. September 2026

  Legal argument mining supports passage classification, retrieval, and argument completion. This work introduces an expert-annotated dataset of 42 U.S. federal tax opinions on corporate reorganizations under I.R.C. {\S}368. To our knowledge, it is the first expert-annotated, tree-structured argument …

• Distributed Legal Infrastructure for a Trustworthy Agentic Web

  Tomer Jordi Chaffer, Victor Jiawei Zhang, Sante Dino Facchini, Botao Amber Hu, Helena Rong, Zihan Guo, Xisen Wang, Carlos Santana, Giovanni De Gasperis · 23. September 2026

  The agentic web marks a structural transition from a human-centered information network to a digital environment populated by artificial intelligence (AI) agents that perceive, decide, and act autonomously. As delegated action unfolds at machine speed, exceeds discrete moments of human judgment, and…

• JEV-as-a-Judge: Accept When Confident, Escalate When Unsure

  Yubo Li, Yidi Miao, Ramayya Krishnan, Rema Padman · 23. September 2026

  LLM-as-a-judge enables evaluation across diverse tasks, but inference cost and confidence reliability become critical at scale. We study whether a decision-only judge can provide an economical first pass and identify when stronger evaluation is needed. Comparing jev-as-a-judge with sixteen generativ…

• EADC: Evaluation of Advanced and Deep-level Compliance in Large Language Models

  Yan Zhang, Ruien Li, Yaoyao Peng, Wanxin Ren, Yijia Zhang, Wusheng Zhang, Guangwen Yang · 23. September 2026

  Large Language Models (LLMs) have been used in various industries. However, ensuring their compliance with complex laws and regulatory frameworks remains a great challenge. Existing evaluation paradigms mainly rely on static benchmarks that suffer from three severe limitations: First, the compliance…

• UK-PRBENCH: A Paragraph-Level Precedent Retrieval Benchmark for United Kingdom Case Law

  Damith Premasiri, Tharindu Ranasinghe · 22. September 2026

  Prior case retrieval (PCR) aims to identify precedent cases relevant to a given query case. Existing PCR benchmarks and methods predominantly operate at the document level, treating entire judgments as the unit of relevance. This formulation is suboptimal for legal practitioners, as judgments addres…

• LLJ Cards: Best practices for the Use of LLMs as Judges

  Khaoula Chehbouni, Melina Medjdoub, Florian Carichon, Golnoosh Farnadi, Jackie Chi Kit Cheung · 22. September 2026

  In recent years, large language models (LLMs) have emerged as a popular alternative for evaluation. Often referred to as LLMs as judges (LLJs), these systems have been widely adopted by researchers and practitioners across a broad range of measurement tasks, driven by their strong performance, scala…

• Efficient LLM Distillation for Bangladesh Legal Context: A Smartphone-Compatible Retrieval-Augmented Generation Model

  MD. Nafis Kamal, Mahadi Hasan Fahim, Talha Ridwan, Nadifa Zaman, Fariha Roushon Florin, Farig Yousuf Sadeque, Saadat Rafid Ahmed · 22. September 2026

  Legal information in Bangladesh is inaccessible to most citizens. Statutory text is English-only, trained lawyers are concentrated in urban centres, and cloud-dependent AI fails where mobile connectivity is unreliable, a setting in which hallucinated legal text causes direct harm. The system address…

• GRACE: Grounded Adversarial Reasoning over Canadian Law

  Jiakang Xu, Wantong Huo, Udom Silparcha, Jonathan H. Chan · 22. September 2026

  Large language models have shown strong performance across a range of legal tasks, but existing benchmarks rarely evaluate the ability to take and defend a legal position, reason under incomplete information, or synthesize multiple statutory provisions. This gap is particularly pronounced for Canadi…

• Directing large language models to follow the letter or spirit of the law

  Peng Qian, Andrew Li, Sam Chen, Sonia K. Murthy, Yonatan Belinkov, Tomer D. Ullman · 22. September 2026

  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, intelligent machines. What is this distinction based on, and how can we develop machines that follow the intention behind a rule? We used targeted a…

• Schematize: An Agentic System for Generating and Refining Information-Extraction Schemas for Legal Research

  Albert Sawczyn, Jakub Binkowski, Kamil Tagowski, {\L}ukasz Augustyniak, Berenika Kaczmarek-Templin, Tomasz Kajdanowicz · 22. September 2026

  Empirical legal research often relies on turning research questions into structured data extracted from large collections of rulings and judgments. Designing the extraction schema and then extracting the data remain a manual, expertise-heavy bottleneck. We present schematize, an open-source multi-ag…

• Beyond Accuracy and Surface Fluency: Risk-Sensitive Evaluation of LLMs for Legal Clause Generation

  Devansh Singh, Sundaraparipurnan Narayanan · 22. September 2026

  Large language models (LLMs) are increasingly used to draft contractual language, yet conventional accuracy or preference-based evaluations are poorly matched to legal drafting. A clause may be fluent and stylistically polished while still omitting an essential carve-out, allocating risk in an unenf…

• Chinese Competitive Debating Dataset and Benchmark

  Zongrui Yang, Haoyuan Li, Zhongsheng Wang, Zhirui Zeng, Pengqian Han, Yi Zhou, Yuting Wang, Jiamou Liu · 21. September 2026

  Debate adjudication requires tracking how arguments develop through interaction, yet existing datasets rarely combine fine-grained debate transcripts with professional judgments collected during real competitions under a shared rubric. We introduce a dataset and benchmark for evaluating large langua…

• Benchmarking LLM Compliance with China AI Generated Content Regulations

  Chenrui Cui, Hongye Fang, Lisha Song, Weichao Chen, Yue Zhu, Gang Xu · 18. September 2026

  The widespread adoption of LLMs has led to escalating content compliance risks. Prior works have contributed to addressing these risks in the English context, downplaying the complexity of Chinese language content. This paper follows China's current AI-Generated content compliance requirements and p…

• Code-as-Auditor: Executable Compliance Reasoning via Regulation-to-Code

  Jisoo Kim, Taeyoon Kwack, Jinwoo Jang, Woo Kyung Kim, Honguk Woo · 18. September 2026

  Large Language Models (LLMs) are increasingly adopted for compliance and legal reasoning tasks, yet their outputs often lack explicit grounding in legal logic and evidence. We present Code-as-Auditor, an LLM-based framework that extends the model's reasoning capability toward structured and evidence…

• Structured Four-Stage Legal Translation: From Natural-Language Traffic Rules to PROLOG

  May Myo Zin, Wachara Fungwacharakorn, Ken Satoh, Katsumi Nitta · 18. September 2026

  Traffic regulations are written for human interpretation and therefore rely on shared background knowledge and flexible phrasing, which inherently introduce ambiguity, context dependence, and semantic underspecification. These linguistic characteristics conflict with the precision required by comput…