We present our systems for SemEval-2026 Task10 (PsyCoMark), addressing conspiracy markerextraction (Subtask 1) and document-level con-spiracy detection (Subtask 2). For marker ex-traction, we formulate the task as multi-labelspan classification over enumerated candidatespans, using IoU≥0.95 positive labeling, hard-negative sampling, and containment-based non-maximum suppression (NMS) with boundary-aware span representations. Document classi-fication is modeled independently using a se-quence classifier with label smoothing and astratified train–validation split. Analysis showsthat entity-like roles (Actor, Victim) are de-tected robustly, while abstract roles (Action,Effect, Evidence) remain sensitive to boundarycriteria. On the official test set, our systemsrank 7th in Subtask 1 (0.2251 macro F1) and12th in Subtask 2 (0.7694 weighted F1).