A defence opinion is an essential step in criminal proceedings, yet it has not been systematically formulated or evaluated as a specific LegalAI task. Grounded in legal principles and practice, we formulate this task as generating a structured defence opinion conditioned jointly on an indictment and the defendant’s stated opinion, which often present conflicting claims. We formalize this setting as a dual-perspective generation problem and introduce DefGen-Bench, a benchmark comprising several Chinese criminal cases with expert-reviewed reference defence opinions. We evaluate eight large language models (LLMs) on this task and observe that existing models tend to mirror the defendant’s opinion, thereby overlooking more appropriate defence strategies. To address this challenge, we propose Knowledge-Enhanced Highlighted Indictment (KHI), a legal knowledge–guided input enhancement method applicable to both open- and closed-source LLMs. Experiments demonstrate consistent improvements across all evaluated LLMs, validating the effectiveness of the proposed approach.

Legal case facts are often lengthy, complex, and difficult to process, posing challenges for legal judgment prediction. Although recent advances leverage large language models (LLMs) for legal reasoning, they face high computational costs and information degradation when handling long cases. Previous approaches, such as architectural modifications and text compression methods, reduce computational complexity to some extent but still struggle to effectively capture legally salient information in complex cases. We propose a legal knowledge–adaptive compression framework for long legal judgment prediction that integrates domain-specific legal knowledge to guide adaptive context compression. Our approach selectively retains legally relevant information while reducing redundant or less informative content, enabling efficient and accurate long-context reasoning. We evaluate the proposed framework on four real-world datasets spanning multiple jurisdictions and languages. Experimental results demonstrate that our method outperforms existing approaches in both prediction performance and computational efficiency.