Implicit feedback in recommender systems contains noisy interactions (e.g., accidental clicks, impulse purchases) that propagate through user-item graphs and contaminate learned representations. Existing explicit denoising methods uniformly rely on training loss to estimate interaction reliability. However, loss is a fundamentally passive signal: it detects noise only after it has caused prediction errors, and it is heavily confounded by training stage, negative sampling, and hard positives. We propose Noise-aware Dimension Learning (NDL), a proactive dimension-adaptive denoising framework. NDL introduces Representation Coherence Detection (RCD), which evaluates the coherence between an interaction representation and the user's historical preference centroid in embedding space, providing an active noise signal independent of loss. RCD and the loss signal are fused via activity-aware weighting (active users rely more on coherence while cold-start users automatically fall back to loss), and the fused estimate drives an interaction-level dimension-adaptive mask that limits the effective representation capacity of unreliable interactions. A lightweight contrastive regularizer further improves embedding uniformity, stabilizing the geometry on which RCD discriminates. On three public benchmarks NDL consistently outperforms state-of-the-art denoising methods, lifting Recall@5 by +17.95% on Amazon-book, +12.33% on Yelp, and +5.63% on MovieLens-1M over the strongest baseline DAR; the core variant without contrastive learning already surpasses DAR on sparser datasets, confirming representation coherence as the primary driver of these gains. NDL is confined to the training loop and is serving-compatible with LightGCN-based production pipelines, adding no online latency or parameters at inference time. Code: https://github.com/GordonDongZHAO/NDL

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