This paper describes a system for SemEval-2026 Task 6 (CLARITY), which focuses on recognizing evasive communication in political interviews. The approach treats the one subtask—determining the clarity level of an answer —as a single joint multi-task problem. A DeBERTa-v3-Large encoder is shared across both tasks, processing the question and answer as a single concatenated sequence. By updating independent linear classification heads simultaneously, the model allows the fine-grained learning signals from the evasion taxonomy to directly inform the broader clarity-level decisions, and vice versa. On the official evaluation set, this joint discriminative system achieves a 0.76 macro F1 score on Task 1. This approach significantly outperforms standard single-task baseline models, hierarchical bi-encoding architectures, and generative large language models like LoRA-tuned LLaMA-3-8B.