Author

Caterina Gallegati

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Preprint Oct 2026

From What to Which: Decoding Modifier Grounding in Frozen MLLMs

As Multimodal Large Language Models (MLLMs) can describe increasingly complex visual scenes, token-level grounding becomes crucial. Yet, when an MLLM generates"the yellow banana on the left", established grounding approaches focus on what is in the image ("banana"), overlooking tokens that help describe which instance...

Barbara Toniella Corradini AI For Good, Istituto Italiano di Tecnologia, Italy et al. · 0 citations

#artificial intelligence Preprint May 2026

APEX: Assumption-free Projection-based Embedding eXamination Metric for Generative Distribution Evaluation

APEX (Assumption-free Projection-based Embedding eXamination), a novel evaluation framework leveraging two open-vocabulary foundation models as feature extractors and the Sliced Wasserstein Distance as similarity measure, proves that it inherits effective scalability to high-dimensional spaces.

Caterina Gallegati, M. Bianchini, Franco Scarselli et al. · 0 citations