Multimodal MRI provides complementary information for brain tumor segmentation, but high accuracy alone does not reveal whether a model relies on clinically appropriate imaging evidence. Radiologists use different sequences for different findings, such as T1ce for enhancing tumor and FLAIR for peritumoral edema. Yet most 3D segmentation networks fuse MRI sequences through fixed connections, regardless of patient, region, or depth. We frame multimodal segmentation as grounded multimodal reasoning and introduce (i) a model agnostic probing framework combining per region modality ablation, Dominant Sensitivity Enhancement (DSE), and gate trajectory analysis to reveal sequence reliance, and (ii) evidence that adaptive Hyper-Connections (HC) induce clinically aligned modality reliance without modality supervision. Across nnU Net, SwinUNETR, and VT-UNet on BraTS 2021, most HC configurations increase reliance on clinically appropriate sequences. This interpretable pattern recurs across all three backbones, suggesting it arises from adaptive connections rather than a single architecture. HC also improves segmentation accuracy at negligible parameter cost. On validation, the best HC configuration increases mean Dice by 0.43 to 0.81 percentage points across the three backbones. On the held-out test set, it reduces mean HD95 by 1.17 to 1.78 mm (approximately 20 to 27 percent) for nnU-Net and VT UNet, adding at most 0.53 percent parameters. Our framework offers an inspectable measure of clinical grounding and extends naturally to multimodal medical foundation models.

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