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This paper introduces M2BIND, a benchmark to evaluate whether vision-language models maintain stable visual-linguistic associations across languages. It finds that binding is not language-invariant, with cross-family and cross-script settings causing significant performance collapse and weaker internal causal binding.
This paper investigates why CLIP struggles with concept binding, showing that while CLIP's binding function is high-complexity, controlled transformer models can learn low-complexity binding functions through multiplicative interactions that generalize better.