Enhancing the moral alignment of Large Language Models (LLMs) is a critical challenge, yet current techniques like RLHF often function as superficial behavioral guardrails, leaving the model’s internal moral representations untouched and susceptible to complex jailbreaks. In this paper, we propose a shift from behavioral intervention to latent space steering by leveraging Moral Foundations Theory (MFT). To ensure these internal representations capture moral concepts shared across different languages rather than superficial linguistic patterns, we first conduct a cross-lingual linear probing analysis, validating a shared yet culturally nuanced moral subspace between Chinese and English. Establishing this robust semantic foundation allows us to extract steerable Moral Vectors with high causal efficacy at both internal and behavioral levels. Finally, we introduce Adaptive Moral Fusion (AMF), a dynamic inference-time intervention that balances the safety-helpfulness trade-off. Empirical results demonstrate that AMF serves as a targeted intrinsic defense, significantly reducing jailbreak success rates on HarmBench while minimizing incorrect refusals on XSTest compared to standard baselines.

Civil judicial cases are highly complicated, posing significant challenges for Large Language Models (LLMs) for Legal Judgment Prediction (LJP). While judges manage this complexity through the dispute focus—a mechanism distilling cases into core issues—existing research largely overlooks this tool in favor of generic reasoning frameworks that lack authentic judicial logic. To bridge this gap, we first introduce FocalLaw, the first dataset aligning full-process Chinese civil judicial data through the dispute focus, comprising 1,000 high-quality cases across six causes of action. Building on this dataset, we examine LLMs’ capability to utilize the dispute focus and uncover a counter-intuitive phenomenon: LLMs fail to leverage the dispute focus even with CoT and SFT, which we identify as the “Clerk Trap”.To solve the problem, we propose FocalJudge, a novel framework that leverages the dispute focus to guide LLMs through a structured, judge-like cognitive workflow. Experimental results demonstrate the effectiveness of FocalJudge and offer valuable insights into the interpretability and reliability of LLMs in the legal domain.