Oshri Naparstek — accepted AI-conference papers

2026

Is the Modality Gap a Bug or a Feature? A Robustness Perspective

Rhea Chowers, Oshri Naparstek, Udi Barzelay, Yair Weiss

CVPR 2026

Many modern multi-modal models (e.g. CLIP) seek an embedding space in which the two modalities are aligned. Somewhat surprisingly, almost all existing models show a strong modality gap: the distribution of images is well-separated from the distribution of texts in the shared embedding space. Despite

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2025

REAL-MM-RAG: A Real-World Multi-Modal Retrieval Benchmark

Navve Wasserman, Roi Pony, Oshri Naparstek, Adi Raz Goldfarb, Eli Schwartz, Udi Barzelay +1

ACL 2025long

Accurate multi-modal document retrieval is crucial for Retrieval-Augmented Generation (RAG), yet existing benchmarks do not fully capture real-world challenges with their current design. We introduce REAL-MM-RAG, an automatically generated benchmark designed to address four key properties essential…

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