This paper presents the system of CCNU forSemEval-2026 Task 10: Psycholinguistic Con-spiracy Marker Extraction and Detection. Thetask requires identifying fine-grained conspir-acy markers that characterize conspiracy think-ing, as well as determining whether a Redditcomment constitutes conspiratorial discourse.For Conspiracy Marker Extraction (Subtask 1),we adopt a Unified Multi-Task Sequence La-beling Framework that jointly models multi-ple conspiracy markers within a single labelingspace. This formulation enables collaborativelearning across marker types while maintaininga compact architecture. For Conspiracy Detec-tion (Subtask 2), we formulate the problem assentence-level classification. Across both sub-tasks, we apply data augmentation powered bylarge language models and ensemble inferenceto improve robustness and generalization. Oursystem achieves strong performance on Sub-task 1, ranking 3rd on the official test set, anddelivers competitive results on Subtask 2.