Text generated by Large Language Models (LLMs) now rivals human writing, raising concerns about its misuse. However, mainstream AI-generated text detection (AGTD) methods primarily target document-level long texts and struggle to generalize effectively to sentence-level short texts. And current sentence-level AGTD (S-AGTD) research faces two significant limitations: (1) lack of a comprehensive evaluation on complex human-AI hybrid content, where human-written text (HWT) and AI-generated text (AGT) alternate irregularly, and (2) failure to incorporate contextual information, which serves as a crucial supplementary feature for identifying the origin of the detected sentence. Therefore, in our work, we propose AutoFill-Refine, a high-quality synthesis strategy for human-AI hybrid texts, and then construct a dedicated S-AGTD benchmark dataset. Besides, we introduce SenDetEX, a novel framework for sentence-level AI-generated text detection via style and context fusion. Extensive experiments demonstrate that SenDetEX significantly outperforms all baseline models in detection accuracy, while exhibiting remarkable transferability and robustness. Source code is available at https://github.com/TristoneJiang/SenDetEX.

Fact verification on knowledge graphs (KGs) uses the structured representation of entities and relations as evidence for validating claims. Previous methods for KG-based fact verification predominantly use natural language inference (NLI) models to predict entailment between claims and KG triples, based on implicit reasoning. We propose Programmatic Graph Reasoning (PGR), a novel framework that integrates large language models (LLMs) for fact verification on KGs. PGR explicitly encodes the reasoning process as a graph reasoning program composed of predefined functions to verify claims step by step. These functions are executed sequentially for graph reasoning and final result prediction. By making the graph reasoning process explicit, PGR ensures more precise and transparent reasoning steps compared to implicit methods. Experimental results on the FactKG dataset demonstrate that PGR achieves state-of-the-art performance with 86.82% accuracy, outperforming all the baseline models. Further analysis confirms the interpretability and effectiveness of our method in handling complex graph reasoning.