Large Reasoning Models(LRMs) have achieved significant breakthroughs over prior large language models (LLMs), but they also entail greater potential safety risks. Existing alignment methods often remain at a shallow level of protection, making them insufficient to address deeper risks and strategic attacks in complex reasoning processes. To bridge this gap, we move beyond the conventional paradigm that treats safety alignment merely as a preventive measure to reduce harmful outputs. Drawing inspiration from human-like introspection and self-correction, we propose Self-Reflection, a technique that introduces a special Self-Reflection token, enabling LRMs to perform Self-Reflection during generation and recover from harmful outputs. Our approach integrates seamlessly into standard post-training paradigms , further enhancing both helpfulness and safety. The experimental results demonstrate that models trained with Self-Reflection not only consistently outperform the baseline in terms of safety (reducing the HCR from 13.8% to 4.1%, nearly a threefold improvement over mainstream approaches), but also achieve substantial advantages in both helpfulness and the safety–helpfulness balance. More importantly, under evaluations involving various adversarial attacks, including a specially designed adaptive attack, the Self-Reflection mechanism significantly enhances model safety without targeted adversarial training.Notice: This paper contains harmful content.

Reasoning segmentation requires models to ground complex, implicit textual queries into precise pixel-level masks. Existing approaches rely on a single segmentation token \<SEG\>, whose hidden state implicitly encodes both semantic reasoning and spatial localization, limiting the model’s ability to explicitly disentangle *what to segment* from *where to segment*. We introduce AnchorSeg, which reformulates reasoning segmentation as a structured conditional generation process over image tokens, conditioned on language grounded query banks. Instead of compressing all semantic reasoning and spatial localization into a single embedding, AnchorSeg constructs an ordered sequence of query banks: latent reasoning tokens that capture intermediate semantic states, and a segmentation anchor token that provides explicit spatial grounding. We model spatial conditioning as a factorized distribution over image tokens, where the anchor query determines localization signals while contextual queries provide semantic modulation. To bridge token-level predictions and pixel-level supervision, we propose Token–Mask Cycle Consistency (TMCC), a bidirectional training objective that enforces alignment across resolutions. By explicitly decoupling spatial grounding from semantic reasoning through structured language grounded query banks, AnchorSeg achieves state-of-the-art results on ReasonSeg test set (67.7% gIoU and 68.1% cIoU). All code and models are publicly available at https://github.com/rui-qian/AnchorSeg.

Cognitive distortion is a critical issue in psychology, with most existing studies based on Burns’ cognitive distortion theory. However, differences in annotation standards lead to variations in building analysis tools, resulting in inconsistent analyses and limiting the generalizability of findings, especially in large-scale and cross-linguistic contexts. To address this issue, we collected all publicly available datasets (four in total) and conducted a series of experiments to evaluate the generalizability of various cross-linguistic models. The results indicate that models exhibit significant performance differences across datasets, highlighting the generalization problem. To mitigate this issue, we propose two solutions. First, we propose a multi-task learning model based on teacher student architecture solution, which demonstrates improved generalization performance in our experiments. Second, we introduce a new dataset (~5,000 samples) derived from reannotating existing open datasets to ensure standardized alignment. The annotation process we provided is interpretable and grounded in psychological principles. Based on this, we constructed large language models with cognitive reasoning chains, enhancing both generalizability and interpretability. This study identifies the generalization challenge in cognitive distortion research, and our experiments show that the proposed solutions significantly improve model performance. The dataset and code are publicly available at: https://github.com/HongzhiQ/CrossLinCD.

With the rise of mental health challenges, social media has become a key platform for emotional expression. Deep learning offers a promising solution for analyzing mental health but lacks flexibility and interpretability. Large language models (LLMs) introduce greater adaptability and can explain their decisions, yet they still underperform deep learning in complex psychological analysis. We present C-IMHI, the first multi-task Chinese social media interpretable mental health instruction dataset (9K samples) with quality control and manual validation. Additionally, we introduce MentalGLM, the first open-source Chinese LLMs for explainable mental health analysis, trained on 50K instructions. The proposed models excelled in three mental health downstream tasks, outperforming or matching deep learning and LLMs. A portion of the generated decision explanations was validated by experts, demonstrating promising accuracy and reliability. We evaluated the proposed models on a clinical dataset, where they significantly outperformed other LLMs, demonstrating their potential for clinical applications. Our models show strong performance, validated across tasks and domains. The decision explanations enhance usability and facilitate better understanding and practical application of the models. Both the constructed dataset and the models are publicly available via: https://github.com/zwzzzQAQ/MentalGLM.

In the current environment, psychological issues are prevalent and widespread, with social media serving as a key outlet for individuals to share their feelings. This results in the generation of vast quantities of data daily, where negative emotions have the potential to precipitate crisis situations. There is a recognized need for models capable of efficient analysis. While pre-trained language models have demonstrated their effectiveness broadly, there’s a noticeable gap in pre-trained models tailored for specialized domains like psychology. To address this, we have collected a huge dataset from Chinese social media platforms and enriched it with publicly available datasets to create a comprehensive database encompassing 3.36 million text entries. To enhance the model’s applicability to psychological text analysis, we integrated psychological lexicons into the pre-training masking mechanism. Building on an existing Chinese language model, we performed adaptive training to develop a model specialized for the psychological domain. We evaluated our model’s performance across six public datasets, where it demonstrated improvements compared to eight other models. Additionally, in the qualitative comparison experiment, our model provided psychologically relevant predictions given the masked sentences. Due to concerns regarding data privacy, the dataset will not be made publicly available. However, we have made the pre-trained models and codes publicly accessible to the community via: https://github.com/zwzzzQAQ/Chinese-MentalBERT.