Abstract: Legal text representation is a fundamental component of legal artificial intelligence systems,directly affecting the performance of downstream tasks such as legal article prediction and case retrieval.However,the professional terminology,complex structure,and reasoning patterns of legal texts often lead to semantic drift in general pre-trained models.Open-source models lack sufficient legal domain knowledge,while closed-source models,despite their strong semantic understanding capabilities,provide representations that are difficult to directly access and reuse.To address these challenges,this paper proposes a cross-model collaborative legal representation framework(CMCLR),which enables collaborative learning between open-source and closed-source models to enhance legal semantic modeling.Specifically,closed-source models are employed to perform dynamic text segmentation and key paragraph identification,producing structured domain-aware signals that guide the fine-tuning of open-source models under collaborative constraints.In addition,unsupervised clustering is introduced to model structural relationships among paragraph-level embeddings,capturing latent semantic associations between legal texts.Experiments conducted on the CAIL2018 legal article classification task demonstrate that CMCLR achieves an accuracy of 90.3%,outperforming representative baseline methods by 2.4 percentage points,while maintaining robust performance across different dataset scales and settings.These results confirm the effectiveness of cross-model collaborative representation learning for deep semantic modeling of legal texts.

Key words: Legal text, Representation, Textual relevance, Legal artificial intelligence, Pretrained models, Cross-model collaborative legal representation(CMCLR)