<b>Bridging the Gap in Chinese Legal Conflict Review: A Dataset, Benchmark Tasks, and Framework</b>

Natural Language Processing has advanced Legal Artificial Intelligence, yet automated review of conflicts between superior and subordinate laws remains understudied. Maintaining consistency across legal hierarchies in Chinese legislation is crucial, yet manual review is labor-intensive and error-prone. To address the absence of systematic research in legal conflict review, this study presents three key contributions. (1) we construct the Chinese Legal Conflict Review dataset (LCR-CN), a novel dataset containing 6,995 annotated legislative clauses with conflict types, explanations, and revision suggestions. (2) we propose three benchmark tasks: (a). Superior Law Retrieval, (b). Conflict Classification, and (c) Conflict Explanation and Revision Generation, to establish a comprehensive evaluation framework for this domain. (3) we fine-tune multiple language models on LCR-CN, achieving a 28.9% improvement in accuracy over baselines, demonstrating the effectiveness of the dataset and the feasibility of automating legislative compliance review. This work introduces the first dedicated dataset (LCR-CN), establishes a standardized task framework, and presents robust baselines for legal conflict review, thereby laying theoretical and methodological foundations for AI-assisted legal conflict resolution.