This paper presents improvements and analysis of the Distribution from Context (DisCo) architecture, developed as part of the "Learning With Disagreements (LeWiDi) 2025" collaborative effort, which leverages soft label distribution prediction and perspective-based evaluation to model inter-annotator disagreements. We extend DisCo to better capture discordant patterns by introducing annotator metadata embeddings, improved input representations, and a multi-objective training loss. Extensive experiments demonstrate significant improvements in both soft and perspective-based evaluation metrics, and we conduct in-depth calibration and error analysis to demonstrate when and why the improved discordance-aware modeling occurs. We also demonstrate that directly optimizing the distribution metric, conditioned on annotator demographics, can better capture disagreements.