通过分离频段信号，让推荐系统更精准保留用户偏好。

Multimodal recommendation improves item representations by combining visual, textual, and collaborative signals, but stronger cross-view alignment does not always improve ranking. Our diagnostics on Amazon Baby show that direct consistency has an effective range: moderate alignment helps, while stronger alignment suppresses recommendation-specific variation. We also observe a clear spectral split: low-frequency components capture shared structure, whereas higher-frequency components retain more private ranking signal. Based on these findings, we propose BRIDGE, a behavior-guided residual integration framework built on dual-frequency graph evidence. BRIDGE separates the model into three parts: DFGE decomposes graph-smoothed ID, visual, and textual channels into spectral bands; BEN converts training-only co-user overlap into signed candidate evidence; and CRI applies that evidence only inside the base top-K candidate set during training and inference. This design keeps the multimodal backbone and localizes behavior evidence to candidate calibration. Experiments on Amazon Baby, Sports, and Electronics show that BRIDGE reaches 0.1128/0.1262/0.0778 Recall@20 and 0.0525/0.0594/0.0385 NDCG@20, outperforming baselines by up to 7.3% in Recall@20 and 14.9% in NDCG@20. Project materials are available at https://lizesheng13.github.io/bridge/.