Yanjing Ren

Yanjing Ren, Reza Ebrahimi, TengTeng Ma

As AI companion platforms such as Replika and Character.AI rapidly grow, concerns about unsafe human-AI interactions have intensified. This study introduces AICompanionBench, to our knowledge the first publicly available benchmark dataset of human-AI companion conversations annotated with fine-grained safety risk categories. The dataset contains 2,123 real-world Replika conversations collected from Reddit and annotated through human-AI collaboration across nine categories: sexual behavior, antisocial behavior, physical aggression, verbal aggression, substance abuse, self-harm and suicide, control, manipulation, and no-harm. Using this benchmark, we evaluate 20 state-of-the-art open-source and closed-source LLMs under an LLM-as-judge framework for detecting unsafe interactions. Results show substantial variation in model performance, with stronger models achieving high overall accuracy but still struggling with nuanced categories such as manipulation, as well as benign conversations that are incorrectly identified as harmful. Our findings suggest that while current LLMs can effectively detect explicit harmful content, they remain limited in identifying implicit unsafe interactions. Overall, our work contributes a new benchmark dataset for AI companionship safety research and offers insights into monitoring AI companion systems using LLMs. The dataset is publicly available at: https://github.com/anonymousresearcher2026/AICompanionBench/blob/main/AICompanionBench.xlsx

Yuanming Ren, Juncheng Zhang, Yanjing Ren et al.

Managing large-scale vector datasets with disk-based approximate nearest neighbor search (ANNS) systems faces critical efficiency challenges stemming from the co-location of vector data and auxiliary index metadata. Our analysis of state-of-the-art ANNS systems reveals that such co-location incurs substantial storage overhead, generates excessive reads during search queries, and causes severe write amplification during updates. We present DecoupleVS, a decoupled vector storage management framework that enables specialized optimizations for vector data and auxiliary index metadata. DecoupleVS incorporates various design techniques for effective compression, data layouts, search queries, and updates, so as to significantly reduce storage space, while maintaining high search and update performance and high search accuracy. Evaluation on real-world public and proprietary billion-scale datasets shows that DecoupleVS reduces storage space by up to 58.7\%, while delivering competitive or improved search query and update performance, compared to state-of-the-art monolithic disk-based ANNS systems.