arXiv:2512.02530v1 Announce Type: new Abstract: The exponential growth of digital content presents significant challenges for content safety. Current moderation systems, often based on single models or fixed pipelines, exhibit limitations in identifying implicit risks and providing interpretable judgment processes. To address these issues, we propose Aetheria, a multimodal interpretable content safety framework based on multi-agent debate and collaboration.Employing a collaborative architecture of five core agents, Aetheria conducts in-depth analysis and adjudication of multimodal content through a dynamic, mutually persuasive debate mechanism, which is grounded by RAG-based knowledge retrieval.Comprehensive experiments on our proposed benchmark (AIR-Bench) validate that Aetheria not only generates detailed and traceable audit reports but also demonstrates significant advantages over baselines in overall content safety accuracy, especially in the identification of implicit risks. This framework establishes a transparent and interpretable paradigm, significantly advancing the field of trustworthy AI content moderation.

Background: In the digital health era, telemedicine has become a key driver of health care reform and innovation globally. Understanding the factors influencing residents’ choices of telemedicine services is crucial for optimizing service design, enhancing user experience, and developing effective policy measures. Objective: This study aims to explore the key factors influencing Chinese residents’ choices of telemedicine services, including consultation fee, physician qualifications, appointment waiting time, scope of services, privacy protection, and service hours. The study also analyzes preference heterogeneity among residents with different demographic characteristics to provide scientific evidence for optimizing telemedicine services in the digital health era. Methods: This study used a mixed methods design combining qualitative interviews and a discrete choice experiment. Interviews identified key telemedicine attributes, informing the discrete choice experiment scenarios. Preferences and willingness to pay were analyzed using mixed logit and latent class models. Results: Residents’ preferences for telemedicine services were primarily shaped by the scope of services, appointment waiting time, and privacy protection, with substantial willingness to pay for more comprehensive, secure, and timely services. The optimal telemedicine services configuration—offering consultation plus prescription, high privacy, immediate access, 24-hour availability, and expert physicians—yielded a maximum willingness to pay of RMB 661.6 (a currency exchange rate of US $1=RMB 7.1803 is applicable). Latent class analysis revealed pronounced heterogeneity: while privacy and service scope remained universally prioritized, older, male, rural, and less-educated residents favored broader coverage, easier platforms, and lower costs; younger, female, and highly educated groups preferred faster, higher-quality, and more privacy-sensitive services. Conclusions: This study reveals key drivers and significant demographic heterogeneity in Chinese residents’ preferences for telemedicine services. Residents demonstrated a high willingness to pay for comprehensive services (eg, “consultation + prescription”), enhanced privacy protection, and shorter appointment waiting times. Additionally, the study innovatively identified 3 distinct resident profiles: “Diverse-Service-Oriented,” “Utility-Oriented,” and “Value-Oriented,” and proposed differentiated optimization strategies to effectively address diverse resident needs, thereby promoting equitable access and efficient adoption of telemedicine services.