Referring Expression Comprehension (REC) localizes the image region described by a natural language expression, underpinning applications such as autonomous navigation, human-robot interaction, and medical image analysis. Under weak supervision, REC is challenged by the visual-linguistic semantic gap and the difficulty of inferring region-text correspondences from coarse image-level signals alone. Moreover, no single visual encoder captures all complementary properties---semantic alignment, fine-grained texture, and boundary awareness---while naively fusing multiple pretrained encoders yields heterogeneous, hard-to-reconcile representations. We propose LaMER, a weakly supervised REC framework that dynamically aggregates four frozen expert encoders through a language-conditioned Cross-Modal Routing Fusion module, adapting expert selection to each referring expression without instance-level bounding-box supervision. A Cell-Text Matching Head with Negative Anchor Augmentation, trained with a multi-objective loss combining contrastive, reconstruction, alignment, and load-balancing terms, further stabilizes optimization. LaMER achieves 77.67\%, 62.81\%, and 69.49\% Acc@IoU\(\geq 0.5\) on RefCOCO, RefCOCO+, and RefCOCOg, surpassing DViN and WeakMCN by up to 16.49 and 14.49 points, with ablations validating each component.

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