Information Router for Mitigating Modality Dominance in Vision-Language Models

The key distinction MoIR draws is between where attention goes and where information actually lives. Most prior work on modality imbalance tries to reweight attention scores after the fact, whereas MoIR intervenes earlier by estimating information density per modality and routing accordingly, which is a structural rather than corrective approach.

Modality dominance is a quiet but persistent problem across the multimodal work Modelwire has been tracking. The K-Token Merging paper from April 16 (arXiv cs.CL) is a useful contrast: that work compresses token sequences in latent space to reduce compute, but compression assumes roughly uniform information value across tokens. MoIR's premise cuts against that assumption directly, suggesting that naive compression or uniform attention could systematically discard the weaker modality's signal. The humor-understanding paper on incongruity resolution also touched on cross-modal tension, though from a cognitive framing rather than an architectural one. MoIR sits in a growing cluster of work asking not just whether models can process multiple modalities, but whether they actually integrate them rather than defaulting to whichever signal is loudest.

The real test is whether MoIR's information-density routing holds up when one modality is deliberately degraded, such as low-resolution images paired with clean text. If ablation results on noisy-input benchmarks appear in follow-up work within the next two quarters, that would confirm the routing mechanism is doing genuine work rather than benefiting from cleaner training distributions.