CARES: How Can Missing-Modality Completion Truly Help Multimodal LLMs?

Multimodal large language models (MLLMs) have advanced rapidly in recent years. However, their performance can degrade when input modalities are missing. Recent work has used modality completion to improve the robustness of MLLM inference. However, the completed modality is not always beneficial and can even be misleading, since modality completion may introduce inaccurate or irrelevant information. This raises a question: how can missing-modality completion truly help MLLMs? To this end, we propose Completion-Aware Routing and Evidence Scaling (CARES), a lightweight framework with two modules: completion-aware routing dynamically estimates the contribution of completed modalities at the token level, while evidence scaling adaptively regulates their influence during reasoning. Experiments across four benchmarks and multiple MLLM backbones validate the effectiveness, versatility, and generalization of the proposed CARES.