AI 中文总结
针对现有学术发现平台支持不足的问题，提出以意图为中心的Sci-Surf系统，结合反馈驱动推荐与多模态论文消化，经评估推荐质量和消化质量有可测量提升。
AI 中文摘要
科学出版物的快速增长使研究人员越来越难以识别相关的新研究并有效理解它们。现有的学术发现平台通常依赖静态主题订阅或基于嵌入的相似度，且仅提供摘要或简短总结，对精细意图建模和深入论文总结的支持有限。我们提出Sci-Surf，这是一个以意图为中心的知识发现系统，将反馈驱动的个性化推荐与多模态博客风格的论文消化相结合。我们的方法通过基于大语言模型（LLM）的用户画像细化用户意图表示，同时生成整合了完整论文中文本和视觉信息的结构化摘要。该演示展示了端到端的学术发现流程，并通过真实用户评估证明了推荐质量和消化质量的可测量提升。具体而言，在为期一个月的在线评估中，口头化画像的整合使与现实世界用户偏好的预测对齐平均提升了10.4%。
英文摘要
The rapid growth of scientific publications makes it increasingly difficult for researchers to identify relevant new studies and effectively comprehend them. Existing academic discovery platforms typically rely on static topic subscriptions or embedding-based similarity and provide only abstracts or short summaries, offering limited support for nuanced intent modeling and in-depth paper summarization. We present Sci-Surf, an intent-centric knowledge discovery system that integrates feedback-driven personalized recommendation with multi-modal blog-style paper digestion. Our approach refines user intent representations through LLM-based user profiling, while generating structured summaries that synthesize textual and visual information from full papers. The demo presents an end-to-end academic discovery pipeline and demonstrates measurable improvements in both recommendation quality and digestion quality through real-user evaluations. Specifically, the integration of verbalized profiles led to a 10.4% average improvement in predictive alignment with real-world user preferences throughout a month-long online evaluation.
