TIB: Sample-wise Tempered Information Bottleneck for Multimodal Attribution beyond Alignment Assumption

arXiv:2611.12997v1 Announce Type: cross Abstract: Multimodal (e.g. vision-language, in this work) attribution aims to interpret the models without access to ground-truth explanatory supervision. Existing multimodal attribution methods typically rely on paired modalities as proxy supervision, implicitly assuming that image-text pairs are semantically aligned. Information bottleneck (IB)-based attribution methods follow this general paradigm and instantiate it through a global sufficiency-compression objective. In realistic multimodal datasets, however, this assumption is frequently violated due

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