We study vocabulary difficulty prediction for second language (L2) learners, a key component for adaptive language learning and assessment. Existing approaches often treat difficulty as an intrinsic property of words or contexts, overlooking representation-dependent variation and learner-specific factors such as L1 transfer.We participate in the BEA 2026 Shared Task Closed Track using the Spanish (L1) subset of the KVL dataset. We propose a two-stage framework that decouples representation learning from learner-aware calibration. Stage 1 constructs diverse representations using multiple pretrained encoders with varied pooling and prediction strategies, capturing complementary aspects of lexical and contextual complexity. Stage 2 models systematic residual errors with psycholinguistic and cross-lingual features, enabling explicit correction of prediction biases.Experiments show that our method outperforms strong baselines, improving RMSE (1.257 -> 0.976) and correlation (0.765 -> 0.857). These results highlight the importance of jointly modeling representation diversity and learner-specific effects. Our system ranked 3rd in the official BEA 2026 Shared Task Closed Track.