A team led by Professor Le-Minh Nguyen of the Computing Science Research Area won first place in Pilot Task 1 - Tort Prediction at the 13th Competition on Legal Information Extraction and Entailment (COLIEE 2026).

COLIEE is an international competition that evaluates the accuracy of AI-based information retrieval and legal reasoning using legal documents such as case law and statutes. The competition features various tasks related to AI and law, including legal information extraction and textual entailment, providing a forum for sharing research findings and approaches. The 13th edition, COLIEE 2026, was held on June 12, 2026, at Singapore Management University (SMU) in Singapore.
The JNLP team led by Professor Nguyen won first place in Pilot Task 1 - Tort Prediction, as well as second place in Pilot Task 2 - Rationale Extraction, Task 1 - Legal Case Retrieval, Task 3 - Statute Retrieval and Entailment, and Task 4 - Legal Textual Entailment. The team also achieved third place in Task 2 - Legal Case Entailment, placing among the top teams in each task.
COLIEE 2026 was also held in conjunction with ICAIL 2026 (21st International Conference on Artificial Intelligence and Law), a major international conference in the field of AI and law.

＊References：COLIEE 2026 
　　　　　　　 ICAIL 2026

■Date Awarded
June 12, 2026

■Team Name and Members
JNLP：Dang Le, Cuong Hoang, Duy-Minh Nguyen-Tran, Nguyen Khac Vu Hiep, Tan-Minh Nguyen, Do Minh Duc, Son T. Luu, Trung Vo, An Trieu, Nguyen Ngoc Minh, Dat Nguyen, The-Hai Nguyen, Trang Pham, Nguyen-Khang Le, Dinh-Truong Do, Vu Tran, and Le-Minh Nguyen

■Title
JNLP at COLIEE 2026: Multi-Stage Hybrid LLM Framework for Case Law Retrieval, Statute Entailment, and Tort Prediction

■Abstract 
This paper presents the JNLP team's approach to addressing these challenges in the Competition on Legal Information Extraction and Entailment (COLIEE) 2026. We address all four main tasks: case law retrieval, case law entailment, statute law retrieval, and statute law entailment, alongside the pilot tasks. For Legal Case Retrieval (Task 1), we improve upon previous approaches by reducing the search space via BM25 and utilizing alternative classification models. For Legal Case Entailment (Task 2), we propose a two-stage architecture that combines dense retrieval with LLM-based reasoning to accurately identify entailing paragraphs. For Statute Retrieval and Entailment (Task 3), we employ a three-stage retrieval pipeline integrated with LLMs, incorporating dynamic few-shot prompting and a multi-agent debate framework. For Legal Textual Entailment (Task 4), we utilize Japanese-specific LLMs with few-shot prompting and self-consistency through majority voting to assess legal validity. Finally, we formulate both Tort Prediction and Rationale Extraction as binary classification tasks, leveraging LLMs to analyze the legal context and predict the target labels. Experimental results highlight the efficiency of our proposed approaches. Notably, we achieved the highest ranking on the pilot task, third place in Task 2, and second place across all other tasks.

■Comment 
COLIEE 2026 is one of the world's premier competitions in Legal AI, bringing together leading research teams to advance intelligent legal reasoning and analysis. We are deeply honored that the Nguyen Lab won first place in Pilot Task 1 - Tort Prediction at COLIEE 2026. We sincerely thank JAIST for providing an outstanding research environment and continuous support for our research. We are also grateful to our students and collaborators for their dedication, hard work, and enthusiasm. This recognition inspires us to continue pursuing cutting-edge research and developing trustworthy AI technologies that contribute to the future of Legal AI.

September 10, 2026