Idiomatic expressions pose a fundamental challenge for multimodal understanding due to their non-compositional semantics, while pretrained vision–language models tend to over-rely on literal visual alignments. We address this issue in the context of the AdMIRe 2.0 multimodal idiomatic image ranking task by introducing CARIM (Category-Aware Reasoning for Idiomatic Multimodality), an inference-time framework that injects structured semantic reasoning without end-to-end retraining.Experiments on the official Codabench leaderboard demonstrate that CARIM achieves competitive Top-1 Accuracy and nDCG across multiple languages. Additional post-competition evaluation on the released test annotations further shows that CARIM maintains robust multilingual performance, highlighting the effectiveness of inference-time category-aware reasoning for multimodal idiomatic grounding.