AI Wizards at EXIST 2026: Hierarchical Soft-Label Learning for Multimodal Sexism Identification in Memes
Authors: Matteo Fasulo, Antonio Gravina, Luca Tedeschini, Luca Babboni
Organizations: Swiss Data Science Center, ETH Zürich, Andreasturm, Andreasstrasse 5, 8092 Zürich, Switzerland · Everest Systems GmbH, Max-Jarecki-Straße 21, 69115 Heidelberg, Germany · Villanova.ai S.P.A, Località Sa Illetta, SS 195 KM 2.3, 09123 Cagliari, Italy · Independent researcher
We present the AI Wizards submission to EXIST 2026 for multimodal sexism identification in memes. The task is composed of three, increasingly harder subtasks. We model them hierarchically as conditional soft-label prediction over empirical annotator distributions. Our system maps fixed Gemini Embedding 2 vision-language representations through a lightweight Gated MLP trained with KL divergence and homoscedastic uncertainty weighting. Our submissions ranked first on Task 2.3 and fourth on Tasks 2.1 and 2.2 on the official Soft-Soft leaderboards. The code is available at https://github.com/NLP-AI-Wizards/EXIST-2026