Researchers have developed a new approach to multimodal sentiment analysis that can handle incomplete or corrupted inputs. Their method, called iterative proxy correction, constructs a language-oriented proxy from non-language modalities and refines it through gated residual correction. This allows the model to balance proxy-based compensation with trustworthy linguistic evidence. The authors claim their approach outperforms existing methods in experiments on several datasets. --- Why it matters: This matters because real-world multimodal inputs are often incomplete or corrupted, which can weaken cross-modal complementarity and introduce misleading information into downstream fusion. Engineers working on AI applications will be interested in this research as it provides a new method for handling incomplete data in multimodal sentiment analysis. Source: https://arxiv.org/abs/2608.19971