This paper presents the submission of team AdaptLinker to the AdaptNER shared task (Subtask 1: Cross-Domain Named Entity Recognition) at KnowledgeGraphEval 2026. To address nested entities and domain shift across 10 unseen target domains, we propose a multi-head token classification framework built on the AraBERT backbone. Our approach inte- grates: (1) 21 parallel linear classifiers to pre- dict overlapping named entities; (2) a Domain Adversarial Neural Network (DANN) using a Gradient Reversal Layer (GRL) to align feature spaces; (3) confidence-gated pseudo-labeling on unlabeled target text; and (4) Stochastic Weight Averaging (SWA). Our final system achieved a Precision of 75.48%, Recall of 71.21%, a Micro F1 of 73.29%, and a Macro F1 of 63.37% on the official blinded test set, outperforming the shared-task baseline (0.7329 vs. 0.622 Micro F1) and ranking 6th out of all participating teams