The widespread adoption of large language models (LLMs) has created an urgent need for accurate and robust detection of AI-generated text. However, existing statistical detectors often rely on a fixed decision boundary, which hinders their ability to generalize across topics, styles, and model variants. In this paper, we propose preference calibration with context-aware thresholding (PC-CAT), a novel framework that integrates preference calibration with context-aware thresholding to improve detection reliability. PC-CAT first applies Simple Preference Optimization (SimPO) to maximize the log-probability gap between paired human-written and AI-generated texts, enhancing discriminability. It then tailors instance-specific thresholds using a lightweight regressor trained with a hybrid centering and ordinal objective. Experiments across diverse domains and generators show that PC-CAT consistently outperforms strong baselines and achieves new state-of-the-art results. Moreover, it exhibits strong robustness under distribution shifts and adversarial attacks. These findings highlight the critical role of adaptive thresholding in advancing the robustness of AI-generated text detectors.

The filter bubble is a notorious issue in Recommender Systems (RSs), characterized by users being confined to a limited corpus of information or content that strengthens and amplifies their pre-established preferences and beliefs. Most existing methods primarily aim to analyze filter bubbles in the relatively static recommendation environment. Nevertheless, the filter bubble phenomenon continues to exacerbate as users interact with the system over time. To address these issues, we propose a novel paradigm, Hypergraph-Aware Multi-Grained Preference Learning to Burst Filter Bubbles in Conversational Recommendation System (HyperCRS), aiming to burst filter bubbles by learning multi-grained user preferences during the dynamic user-system interactions via natural language conversations. HyperCRS develops Multi-Grained Hypergraph (user-, item-, and attribute-grained) to explore diverse relations and capture high-order connectivity. It employs Hypergraph-Empowered Policy Learning, which includes Multi-Grained Preference Modeling to model user preferences and Preference-based Decision Making to disrupt filter bubbles during user interactions. Extensive results on four publicly CRS-based datasets show that HyperCRS achieves new state-of-the-art performance, and the superior of bursting filter bubbles in the CRS.