Background: As generative AI (GenAI) becomes increasingly prevalent, its impact on physician mental health has garnered significant attention; yet, empirical evidence remains limited. Objective: This study aims to investigate the correlations between the usage frequency of GenAI, perceived usefulness (PU), and perceived risk (PR) of GenAI with physicians’ burnout and professional fulfillment. Methods: A mixed methods design was used, integrating a quantitative survey of physicians across 4 regions in China with in-depth qualitative interviews to elucidate the underlying psychological mechanisms. The quantitative component involved a cross-sectional survey of 961 physicians, with the questionnaire collecting data on demographic and professional characteristics, socioeconomic status, GenAI usage frequency, PU, and PR. Semistructured interviews with 10 physicians were used for in-depth mining. Multivariable logistic and linear regression models with province-level fixed effects were fitted to examine the association between usage of GenAI, PU, PR, and physicians’ burnout and fulfillment. Stratified analyses were further performed to explore the moderating effect of demographic and clinical characteristics. Results: Quantitative analysis revealed no direct correlation between GenAI usage frequency and burnout. However, PU was positively associated with professional fulfillment (odds ratio [OR] 1.56, 95% CI 1.17-2.08; P=.003), whereas PR was associated with a higher likelihood of burnout (OR 1.80, 95% CI 1.46-2.21; P<.001). Stratified analyses showed that for physicians working ≥3 night shifts per week, GenAI usage was associated with higher odds of burnout, although the estimate was imprecise (OR 13.96, 95% CI 2.40-81.04; P=.003). The qualitative findings further suggested that the benefits of using GenAI may be offset by the additional burden. The PU of GenAI was perceived to enhance professional fulfillment by bolstering self-efficacy, whereas the PR of GenAI was linked to heightened burnout rooted in unclear boundaries of responsibilities and rights, as well as challenges to professional identity. Conclusions: The GenAI revolution in medicine is as much a psychological transition as it is a technological one. GenAI use is not directly associated with improved psychological states among clinicians. The PU of GenAI relates to professional fulfillment, and the PR concerns correspond to elevated burnout. Sustaining clinician well-being during this digital shift thus parallels a dual requirement, balancing the potential for professional fulfillment tied to GenAI utility against the concurrent verification fatigue and legal uncertainty cluster around clinician burnout.

arXiv:2603.04128v1 Announce Type: cross Abstract: Developing Audio-Visual Large Language Models (AV-LLMs) for unified scene understanding is pivotal in multimodal intelligence. While instruction tuning enables pre-trained models with multi-task abilities, we observe that conventional multi-task unification methods often suffer from severe negative transfer, where nearly 55% of tasks degrade compared to single-task training. We attribute this phenomenon to audio-visual task heterogeneity, characterized by disparate task granularity and divergent capability demands, which lead to negative interference under joint training. To tackle this, we present Crab$^{+}$, a scalable and unified audio-visual scene understanding model that addresses task heterogeneity through explicit cooperation from both data and model perspectives. On the data side, we introduce AV-UIE v2, a comprehensive Audio-Visual Unified Instruction-tuning dataset with Explicit reasoning processes. It contains approximately 222K samples spanning 17 datasets and 7 tasks, enabling the model to capture cross-task relationships at different levels of granularity. On the model side, we design a unified interface to align heterogeneous task formulations, and propose Interaction-aware LoRA (I-LoRA), which explicitly models inter-task relationships via dynamic routing to coordinate distinct audio-visual interaction patterns, mitigating parameter interference. Extensive experiments show Crab$^{+}$ covers broader tasks than existing unified models while outperforming specialized models on various benchmarks. We successfully reverse the negative transfer trend, achieving positive transfer where multi-task learning surpasses single-task baselines in nearly 88% of tasks. These results hold across diverse AV-LLM paradigms and are validated through in-depth visualization, positioning Crab$^{+}$ as a robust step towards holistic audio-visual scene understanding.

arXiv:2510.18573v2 Announce Type: replace-cross Abstract: We present Kaleido, a subject-to-video~(S2V) generation framework, which aims to synthesize subject-consistent videos conditioned on multiple reference images of target subjects. Despite recent progress in S2V generation models, existing approaches remain inadequate at maintaining multi-subject consistency and at handling background disentanglement, often resulting in lower reference fidelity and semantic drift under multi-image conditioning. These shortcomings can be attributed to several factors. Primarily, the training dataset suffers from a lack of diversity and high-quality samples, as well as cross-paired data, i.e., paired samples whose components originate from different instances. In addition, the current mechanism for integrating multiple reference images is suboptimal, potentially resulting in the confusion of multiple subjects. To overcome these limitations, we propose a dedicated data construction pipeline, incorporating low-quality sample filtering and diverse data synthesis, to produce consistency-preserving training data. Moreover, we introduce Reference Rotary Positional Encoding (R-RoPE) to process reference images, enabling stable and precise multi-image integration. Extensive experiments across numerous benchmarks demonstrate that Kaleido significantly outperforms previous methods in consistency, fidelity, and generalization, marking an advance in S2V generation.