The integration of additional modalities increases the susceptibility of large vision-language models (LVLMs) to safety risks, such as jailbreak attacks, compared to their language-only counterparts. While existing research primarily focuses on post-hoc alignment techniques, the underlying safety mechanisms within LVLMs remain largely unexplored. In this work , we investigate whether LVLMs inherently encode safety-relevant signals within their internal activations during inference. Our findings reveal that LVLMs exhibit distinct activation patterns when processing unsafe prompts, which can be leveraged to detect and mitigate adversarial inputs without requiring extensive fine-tuning. Building on this insight, we introduce HiddenDetect, a novel tuning-free framework that harnesses internal model activations to enhance safety. Experimental results show that HiddenDetect surpasses state-of-the-art methods in detecting jailbreak attacks against LVLMs. By utilizing intrinsic safety-aware patterns, our method provides an efficient and scalable solution for strengthening LVLM robustness against multimodal threats. Our code and data will be released publicly.

Large language models (LLMs) have achieved remarkable success across diverse domains. However, their potential as effective language teachers—particularly in complex pedagogical scenarios like teaching Chinese as a second language—remains inadequately assessed. To address this gap, we propose the first pedagogical competence benchmark for LLMs, rigorously evaluating their performance against international standards for Chinese language teachers. Our framework spans three core dimensions: (1) basic knowledge evaluation, covering 32 subtopics across five major categories; (2) international teacher examination, based on data collected from international Chinese teacher certification exams; and (3) teaching practice evaluation, where target LLMs summarize knowledge points and design instructional content for student models, followed by testing the student models to assess the LLM’s ability to distill and teach key concepts.We conduct a comprehensive evaluation of 13 latest multilingual and Chinese LLMs. While most models demonstrate promising pedagogical potential, there remains substantial room for improvement in their teaching capabilities. This study contributes to the development of AI-assisted language education tools capable of rivaling human teaching excellence. The benchmark dataset and evaluation scripts used in this study are publicly available at https://github.com/Line-Kite/CLTE.

Universal Information Extraction (UIE) has been introduced as a unified framework for various Information Extraction (IE) tasks and has achieved widespread success. Despite this, UIE models have limitations. For example, they rely heavily on span boundaries in the data during training, which does not reflect the reality of span annotation challenges. Slight adjustments to positions can also meet requirements. Additionally, UIE models lack attention to the limited span length feature in IE. To address these deficiencies, we propose the Fuzzy Span Universal Information Extraction (FSUIE) framework. Specifically, our contribution consists of two concepts: fuzzy span loss and fuzzy span attention. Our experimental results on a series of main IE tasks show significant improvement compared to the baseline, especially in terms of fast convergence and strong performance with small amounts of data and training epochs. These results demonstrate the effectiveness and generalization of FSUIE in different tasks, settings, and scenarios.