We present the NUST PsyAI system for SemEval-2026 Task 10 (PsyCoMark), targeting document-level conspiracy detection and character-level psycholinguistic marker extraction from Reddit discourse. Our system ranks 7th in Extraction and 8th in Detection on the leaderboard. We benchmark feature-based and transformer approaches, adopting RoBERTalarge with LoRA for parameter-efficient finetuning. For detection, RB-DET-LoRA outperforms all baselines, achieving weighted F1 0.79 (dev) and 0.76 (test), with robust generalization under blinded evaluation. For extraction, we contrast a unified multi-type BIO scheme with a decomposed per-type setup; the latter mitigates cross-label interference and improves boundary consistency, reaching Overlap F1 of 0.16 (dev) and 0.21 (test). Results reveal a clear asymmetry: detection benefits from contextual semantic modeling, while extraction is limited by sparse supervision and boundary-sensitive evaluation.